{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example workflow using ANUGA unstructured grid\n",
    "### Kyle Wright and Jay Hariharan, July 2020\n",
    "\n",
    "This workbook provides an example workflow for running particles on an unstructured model output (i.e. flow variables are from a non-Cartesian grid). We make use of several of the geospatial functions in `particle_track.py` and others in `routines.py`, in order to show how to grid hydrodynamic input files, convert UTM coordinates into (and out of) the array coordinates used in routing the particles, as well as how to compute exposure times to a region of interest. Hopefully this example in conjunction with other examples can provide information on how users can adapt these codes to their use-case.\n",
    "\n",
    "To demonstrate this functionality, we make use of outputs from the hydrodynamic model ANUGA (github.com/GeoscienceAustralia/anuga_core/), which solves the 2D shallow-water equations on a triangular mesh. We've extracted outputs from a previous example model run and included these as text files in the repository, so as to avoid importing any dependencies not required by this distribution. If the user is also using ANUGA flow-fields, there is a a commented-out block of code below demonstrating how we extracted the input files for use in this workbook. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import necessary dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy\n",
    "import matplotlib\n",
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "import json\n",
    "import dorado\n",
    "import dorado.particle_track as pt\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load in model outputs\n",
    "\n",
    "If we were starting directly from an ANUGA output file, this is where we would import the outputs from the model run. We have included these files directly with the distribution, but for anyone interested in repeating the steps we used to generate these files, uncomment the block of code below.\n",
    "\n",
    "Here, `path2file` should point to the ANUGA output file (e.g. `./model_output.sww`). This output is a NetCDF file with flow variables (e.g. `depth`, `xmom`, `stage`) listed by triangle index, along with the centroid coordinates (`x`, `y`) of that triangle. In our case, those coordinates are in meters UTM, which will be relevant later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Import anuga to access functions (NOTE: Anuga requires Python 2.7!)\n",
    "# import anuga\n",
    "\n",
    "# # Folder name of run to analyze:\n",
    "# path2file = 'examples/example_model.sww'\n",
    "\n",
    "# # Extract the files from NetCDF using Anuga's `get_centroids` function:\n",
    "# swwvals = anuga.utilities.plot_utils.get_centroids(path2file, timeSlices = 'last')\n",
    "# # Query values: time, x, y, stage, elev, height, xmom, ymom, xvel, yvel, friction, vel, etc\n",
    "\n",
    "# # Here, since we are only interested in saving variables, we migrate variables to a dictionary:\n",
    "# # Make sure to filter out NaN's before converting to lists, if there are any\n",
    "# unstructured = dict()\n",
    "# unstructured['x'] = swwvals.x.tolist()\n",
    "# unstructured['y'] = swwvals.y.tolist()\n",
    "# unstructured['depth'] = swwvals.height[0].tolist()\n",
    "# unstructured['stage'] = swwvals.stage[0].tolist()\n",
    "# unstructured['qx'] = swwvals.xmom[0].tolist()\n",
    "# unstructured['qy'] = swwvals.ymom[0].tolist()\n",
    "\n",
    "# # And then we save this dictionary into a json (text) file for later import\n",
    "# json.dump(unstructured, open('unstructured_model.txt', 'w'))\n",
    "# # This generates the file imported in this workbook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we will skip the above step and just import the `unstructured_model.txt` dictionary.\n",
    "\n",
    "**Note**: We have chosen to save/import the variables `depth`, `stage`, `qx`, and `qy` in this application. However, we could have chosen to save and use the fields `topography`, `u`, and `v` (or in ANUGA's terminology, `elev`, `xvel`, and `yvel`). The particle tracking code accepts any of these inputs, as long as you provide enough information to calculate the water surface slope, depth of the water column, and the two components of inertia. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "unstructured = json.load(open('unstructured_model.txt'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert data and coordinates for particle routing\n",
    "\n",
    "Now that we have the data we need, we can convert it into the format needed by `dorado`. This will include gridding the hydrodynamic outputs and transforming our geospatial coordinates into \"array index\" coordinates.\n",
    "\n",
    "First, let's combine our $(x,y)$ coordinates into a list of tuples. This is the expected format for coordinates in the following functions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(624422.25, 625031.9375, 3346870.0, 3347107.75)\n",
      "103558\n"
     ]
    }
   ],
   "source": [
    "# Use list comprehension to convert into tuples\n",
    "coordinates = [(unstructured['x'][i], unstructured['y'][i]) for i in list(range(len(unstructured['x'])))]\n",
    "\n",
    "# Let's see the extent of our domain\n",
    "print(min(unstructured['x']), max(unstructured['x']), \n",
    "      min(unstructured['y']), max(unstructured['y']))\n",
    "# As well as our number of data points\n",
    "print(len(unstructured['x']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's grid our unstructured data into a uniform grid. For this, we make use of the function `particle_track.unstruct2grid()`, which uses inverse-distance-weighted interpolation to create a Cartesian grid the same size as our model's extent. To use this function, we need to provide:\n",
    "- Our list of `coordinates` (as tuples).\n",
    "- The unstructured data we want to be gridded (here we start with `depth`).\n",
    "- The desired grid size of the resulting rasters (here we're using $1 m$, because the test model was on very high-resolution lidar data).\n",
    "- The number of $k$ nearest neighbors to use in the interpolation. If $k=1$, we use only the nearest datapoint, whereas higher values (default is $k=3$) interpolate the data into a smoother result.\n",
    "\n",
    "The underlying code relies on `scipy` to build a `cKDTree` of our unstructured data, which maps the datapoints onto a uniform array. `cKDTree` is much faster than other gridding functions (e.g. `scipy.interpolate.griddata`), but building the tree can still be very slow if the dataset is very large or if the desired grid size is very small.  \n",
    "\n",
    "The outputs of `unstruct2grid` are:\n",
    "- The resulting interpolation function `myInterp` (after building the nearest-distance tree), which will be considerably faster than calling `unstruct2grid` again if we are gridding additional datasets. This function assumes data have the same coordinates, grid size, and $k$.\n",
    "- A gridded array of our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Gridded Depth Array')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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wn+afB1nmcfQ/mBT0HJjq9D73yl1yRNv5ZpZL891c3sto+kRiuRP4WS/7DPd1y7bIzHLJaCev3aMjUI+xkGxW3IC/1rk8numjjLEwlbiP/A7nXL/9dM1sIfHc0ANyzj1K/PeeZ2YnhJtZUVD/Ij2naBIRkSwKbkVkVIRmd99MrLrAzN44VvUZolWJ5Yty2D+XH+PJjPZwlXlnYvkCM6vO4Zj+PEGc1cwD3tnfziH4fdMBnnPYmNk76FnnH/bR33O4r1u2QgaYC9fM8oELEqvu62PX9qxjDlrhOp6RWPUB59zxuTzoeS1WmNnho1v7HpIZ0+LQF7Y/HxjCObKzt+8ibs5+r3Mu10HJREQmJAW3IjKavgo8lnh9o5mdM1aVGYLk3JLnm1mfc9ia2fn4eU8HkszEHG9mfU6fY2avo+eP/b7cCmwJyyXADbkGQGY2OTu7GJpBXp9YdaWZ9ZeR+govHSxrTJjZe4GViVWNwBf62H1Yr1sf/nWAJu2fIm4+3QHc0Md+yVYQh+Zw3rF0IfFgW7uBu3M90Dn3IPEI6cbAczyPpCbi6aPKiZsov4SZncDQgtsbift+v4N4/lvQQFIiIgNScCsio8Y5145vYrcjrCoFbjezG82sz/kizWySma2gZ1ZjLNxDPPqpAbeZ2RuydzKzd+KDwc7sbdmcc5voOWfldb0FuGZ2CnA7/t/truztWWWm8T+so4GITgN+b2bH9nWMmR1lZl/GNwOd1csuXycOqF4B3GNmc5I7mFmBmX0FuIIc3vtIMbMiMzvbzH6DD2yjuVrbgfP6GqV3hK5bUic+EL3HzHr06Q2f8X/Az50a+fd+ppxKzt/cbzb4IJBsbXCLc26wn40bE8vv6XOvERZu8iRHcL7ezF4yOF5oKXAXfuqeQY0aHqan+ml4WUo8D/Yu/PdfRET6oXluRWRUOee2hR+EtwNH44O1dwHvMrMdwB/xP+Ra8T/uZuB/4GVnu+6jZ/ZqxDnnnJldCjyMn3+0Evi1ma3H90tN4fvERnN5fhS4JoeiPwa8Fj8iahE+Y3glfkTfNP46RcH/NcCbgX6bZzrn7jWzDwDXhnodD6wzs8347PkL+KBvOrAY6LcJrnNup5ldDtwcyns1sMnMVgNPA1Pwc5FOwwffnwOuzuG9D0WlmX0ra10Jfu7fGfj3k51xfQK4KPRr7NNwX7cstwCz8aM2P2Fm9+MzxZOB19Nznt2HgS/2U9atxE19v2JmZwB/wWd7I19yzr0wiPoNOzM7Gv/5jfQ3t21ffgx8NizPNbPXOeceOuDKDc2/4pvcF+Mz7GvM7GHgKXx/3NcS3+T4HvBK+snw9uG79BxQCuBHQ7gpICIy8Tjn9NBDDz1G/YEPRj6NH6TJ5fh4Efg98KZ+yp2Z2L9+EPVZkThu1QD7HgVsHaCeXwr7ZtYPUGYdfjCZ/t7//+J/QNcn1s0coNyT8D+8c73GjwO1/ZT3Lvx8nH0dvxd4y2CuZ45/n4sH8R6Sj6eATwLFgzzfsFy3rHqvxGe9HxqgrF8DUwaoXx7wuwHKmZnYf2ZifU7fi8F8zvop4+uJMp4BbIjl/DFRzneytq1MbLt4EGUO6f3hg9vWAa79d/B9rFcl1q0YxDnWZ5X3qgP9Dumhhx56TISHMrciMiacc234jNM38RmoU/AZrWp8lnYS0IwPftcDfwDudM49PTY1jjnn/mxmi/BNWN+Bz84U4keBfRD/43tQmSXn3PaQ0b4E30fxSHxG72/4H/bfd879CmDgcWx6lHufmc3HZ3vPxmcip+MzrW34Pqib8AHXr5xz6/sqK5T3EzN7APj7UN5hQDewHd8U89vOZ+dX5FzJA/ciPuDei8+sbsRfs4eAh51zbrAFDvd1S5T7NzNbjh887EJgAX5E3d3AI8BK59ytOZTTbWan46eJeSuwCN+SoKDfA0eRmeXRs4/4T4bytwh+DLwmLL/DzD7ifDeHUeecu93MjgQ+DryB+DsQff9XOud+D4P7rma5lTjjvcY595cDqrSIyARhQ/9/RkRERPpjZhcTD8b1A+fcxWNXGxkvzGwVcXPmy51zGkxKRCQHGlBKRERE5CARBht7fXi5D7hpDKsjIjKuKLgVEREROXh8GD8aO8CPnR9BWUREcqDgVkREROQgYGbHAO8PL18kt9HWRUQk0IBSIiIiImPAzA7BT3M0CT+917nE01j9wPl5sEVEJEcKbkVERETGRjnwiV7WP9XHehER6YeaJYuIiIiMvW783Lv/CbzWOffC2FZHRGT8mZBTAZnZYfSco7ED2AL8H/A/Yf5NERERERERGScmXHBrZmfjJ4Mv72OXJ4GznHNPj16tRERERERE5EBMqODWzI4GHgJK8HPHfRm4DygG3glcHnbdBBzrnNs3FvUUERERERGRwZlowe19wAp8v5bXO+ceztr+D8DV4eWVzrl/Gd0aioiIiIiIyFBMmODWzI4F1oWX33HOXdHLPpOAx4EFwAtAjXOua/RqKSIiIiIiIkMxkUZLPi+xfH1vOzjnXgR+GF4egs/yioiIiIiIyEFuIgW3J4bnVuCP/ey3OrG8bOSqIyIiIiIiIsMlb6wrMIoWhOctzrnufvbb1Msxw8LMCoFF4eVOID2c5YuIiIiIvEylgGlheYNzrmMsKyMHpwkR3JpZETA1vHy2v32dcy+YWStQCtQN8jwzBtjlKOCXgylTRERERER6OBZ4ZKwrIQefCRHcAmWJ5Vym94mC28mDPM/2Qe4vIiIiIiLjmJnlAdMPsJjnB2hdKjmYKMFtUWK5M4f9o2YOxSNQl+AyesbcIiIiIiLSuxbguujFzjGsSG+mc+BJrjoGaGEqA5sowW17Yrkgh/0Lw/P+QZ5noGbM04E/+MUyYMogixcRERERmfA0bo30aqIEty2J5VyaGpeG51yaMGc45/q922JmgylORERERETGkfeTe/pqL/CdEazLRDQhglvnXLuZ7cIPKtXvoE9mdghxcKs+tCIiIiIikpNKoCLHfSdEIDbKJtI8t0+E5zmh03df5vdyjIiIiIiIiBzEJlJw+0B4LgWO6We/5YnlB0euOiIiIiIi8nKSN8iHDK+JFNzelli+pLcdzGwS8J7wshm4b6QrJSIiIiIiLw95QH6ODwW3w2/CBLfOuXXA/eHlpWb22l52+wSwICz/p3Oua1QqJyIiIiIi454yt2Nrol3Tj+CbGhcD95jZv+Gzs8XAO4H3hf2eAv5jTGooIiIiIiLjUpS5zXVfGV4T6po65x4zs/OBG/CjdP9bL7s9BZztnGvpZZuIiIiIiEivBpORnVCB2CiZcNfUOXeHmR2Fz+KejZ8aqBPYAtwMfMs51zaGVRQRERERkXFImduxNSGvqXPur8DHw0NEREREROSAKXM7tnRNRUREREREhkE0EnKu+8rwUnArIiIiIiIyDNQseWzpmoqIiIiIiAwDNUseW7qmIiIiIiIiw0CZ27E1aawrICIiIiIiInKgdMNARERERERkGKhZ8tjSNRURERERERkGapY8tnRN5SA1MzzXh+fK8Lx71GsiIiIiIpILZW7Hlq6pHIQ+DEwJy98EFgCHw+SFsA/ggbDtUGALUAP8Ctg/yvUUEREREYkpczu2dE1lFBwanpPZ1+fC8nv90ztnwU+3ATXw6RI41a8uX3YBe5ZNh+Nh2jefYSq7OJ37AbiPk/jT1lPhPIPHa2B+jT9oUxtw9Yi/KxERERGRJGVux5auqYiIiIiIyDBQ5nZs6ZrKMDkBeLCX9QuA8+OXM4AioBl4G/DpLgCmHf4MJT/p4Hz+nSp28WZuA+AOzuXEP9zPQ7yOMlpYwX1s4KhMcafOvpcNGxZRQTMP8ToAnp01F46/Cn76G3yWuBH11RURERGRkabM7dgy59xY12HCMLMZwHb/6mPE/UrHo+ie1AXADli8FNa3hXUbgGqonwHdeVDUAcCZh95FI9XMop5GaiihjffzHQBKaOOVPEkJ+5m+bQ9sDkW1+qf2N0BbaRGVO9rZWT0ZgBTdbGUOzVTwGIuZSlNYn+ZeTuWGrZeFJsu/IG4Sfe8IXhMRERERGRl7gW9EL+qcc8+OYWV6SP7Gfwh4RY7H/Q1CagY4yN7TeKUbBjIIS2HGmX7xVGANcBtMnjGZfbu6mHb4LgAu4W5O5PdsZQ4ltPE6HgLgBSqYRT3Ve3aTvx6YS/wJXIMPZDuA7sQp84BCKNoGReXtuFIoa90HQDpvErMLt9BCGQvZSIo0AFuYzWy2sGj2n6nfMItrv/Rx+HYo79kt+EyuBp8SERERkeGlZsljS5nbUTR+MrdTgK6w/GEoKvHBYTcw1a9d/qa76aSAU7iXNHmczq+ZwxYAuklx2O6dWBOQBgpDUduAUmBPeHQTf6ujwDYV9ikK69vD69qwb3XYD3Dl0FEIRdGxhYmySmFvVT4/Tl1ACfv5Gp8E4PHPHwtfbASuHY4LJSIiIiKjYnxkbh/F/2zNRQPwmvjlQfWexivdMJjwFoTn8/FT7CzzWdkouPwcvHbp7yijhdls5STuA6CFMs7iLrZTx5LdjwNgO8IxzwBNoYzWxKlK8cFqFNQmg9jD8AFvYdYxKXzg2gBMTuwPWAMUNQFVYXsU3Jb6Okyp6uL8I2+imQqq8FnlVf96Ej/+1wt4fu5VsOXPwM8Gd7lERERERPqQl4J8y3FfB6HhoQyTSWNdAREREREREZEDpcztRDbnqjgTejzkf+Uoupodkya38fWajwOQJsUiNlBNI7PS9XSnUgBUPt4OhTCdPX4g4j34rCz4zGs6vI6aGYP/tBWF1634MZ6iT2AlUAfsAGogjA3ls7wN4TlNnN2NtnUQDz7VkXhvIQNcmddOZffzHMHzACw6cgOn82uu33wJW5nNOvt4OOA6fHMXEREREZGhycuDPGVux4yC2wnpTJi/FG6Dt867AYATuZ+1LOVjVd9gXvpJynZ39TjCNhOaCCfWR02LU/hgMhoIKtnPNtn0uBQoD/tVh+N9rEx7OXQW5ZOqSlPS+iIWHQ/xLD6todwoiE7Hx5NHz+C2A9/zoSFsC4MlH755J4e372TpO9ZyRerbrJu63G/YVYkfZKrn+xYRERERyVXeJMjPsW1s3osjW5eJSMHthDAzPJ8LdMN1NZx86Z28kTtYxAYAFvMY/2/Pd8nfwUv7ye72h/UYxbgb39cVfMAaDeoEcbBbFZ6j4DZaTgF50FUILeVxJ9o0eZCCdF6HLxNIdaehFkobXvTn2AHsCwfUEmdzk3WOsrypsL4VH+QS6lgJU37bxY2LL2Xhzo0AfP6Sr8HKzcCP+7uQIiIiIiJ9ysvzAW5O+yq4HXYKbl/WZgI1MDlM33MNMAOuOf39nMsvSJHm8E07/baouW/UdDgSBYnZwW0e8UjGHfigM/o0VeEzpZMTZeKXXR50p+KgNp34CHZSQIo0bakS0iElm0r5thoFlXvIK4eOWT7DC1DQ3kXRDnwgvA8/tRD4QDYaqTn7E94d6hpGa77gLB/Mbrl+Nj9YeUI/11JEREREpH/5Kf/IaV81SR52Cm5flmbi/7SnwUdreM03HgDgfG7iElYybfM+n40tJQ5YoyA0Gsk4+lJG2dhov2TgG2VEk0EvodzDoL0U2kqL6CRuY9xBAQB5pDPL2Qrp7LEtRZod5ZWZ1wV0+rJKC2mb1c0hu9ux5HupxGd1G/ABeGHifZTjR3PeC/wOjtjs++Je9pHr+MENH4CLGoEHe62XiIiIiEi/UsS/o2XUKbgVEREREREZDskk0UByHHhKcqfg9qBXSTyiUj7xn2x/eP2asM+hwIywbS2wFL4Fr/ngA3ySrwFQww7KWvfF/U9LiUcybsVnOdPE2VvwzXfzEvu0JtZHTZKj/rXVYVtofpzOm0QLZZkmxskmyGm6e7xOhdcpuumgoEe2NylFml2hs28J+ymjhRcqobC0k1S377hQFI3GPJe4P26y3nOBbWFdGGl52T2P8t0L3837pv8ITlXmVkRERESGIIwtczAws9cAZwAnAkfif6134aOBh4DvO+fuH4bzXAVcmePuJznnVh3oOftykFx68YqBKfg/y4rEur34+XG2AI1h/SIgH/Jm+Jj2HAiz3cCypXCO432z/4uFbOQkVgEwfXOYtica1AlJY52/AAAgAElEQVR8sAdxs90Oek7f0xqq04oPeqP902HfPHxwW0pmhGRXBS3l+bSlSsKuvX/MCujIBLFp8qhnJsW0AWTWF9BBCftJk6INX14qtD/upJBGqqmlgcLCTsryWkLJXXRW5fs+uaX4PrbR+6lNvIe9xIF+A1TQTNGS3bTzDuD/eq2ziIiIiEif8jgoIiwzWw28vpdNBfhUz1zgvWb2I+Ay51znaNZvpBwEl36iOgfYGpaLw2MZPgu7GaaG0ZF2AVPx91qKquI+r834+HcF/q84s50Zh24H4Dx+zhu5gzq2M//xv8bT8mymZ2AbzUUbLXcnHjsSVY3mk+1I7N8dyqkGFvg6uBBAtpVOoi1Vkuk3GwWjKdLkhcm8UqHPbTMVAKxlKZ0U0EYJM6mnMxxbTSP1zGI/xVTRRBvxYFM1NNJGCU1MpYpdzErV+wqUQjcpSkr3k+reR34lceY2jQ9w84AHiPvpPgFv33YnP5z1W+782tvhk4iIiIiIDM4kcm+WPLKjJR8anhuAm4H78SPPpIDXAp8I+7wb/8v4gmE676IBtm8bpvP0SsHtWJn9Sjj2ZL8cBVoX44PWJQthPfG2ycCp+EAsys42A0XwqeVfIE2KahqpCRHpPJ5kceufKNpM3KIZ4uxrmGYnE6hCz7lqk814o1HcusP2V4TXC8hkQ10IuFvK/SjGL6Qq4tGOSZMKheynhBRpmqmgjBZaKOMuzgLgMRazlTlU00gZLdThA/Uy9lFGC2W00EBtKM2X3Ug1hXRSQTM1NLKdOoDQqLmDfZTRWV7AoR2745sC0XsuBY4gDuLzgAfhpFn3seqKFey75SpYszJsrEdEREREZECDydy6kawIm4B/Am51zmWPy7wmZGwfBF4JvMvMrh2OJsrOuccPtIwDoeB2rKx0cSfy54FPA3kwbd4z7G8tJvU2/xnc82wN75p9PbU0UEAn13MxAO/hR/w9/0UBnaxnMa/reMjPBQvwCD44jZriZk/hEz1H88FCnN3tyFofHVuOz3iGMh+du4AU3dSxncIO34qhOxXfpkqTRycFmWbGfl2KjSzkMRaTJo/t1LGV2QA0UkPDnldQUN5BE1OpoDlznM/oFlNGS6ZsgDaKacGPwJwizVR2hSqnmEoT3aTooJCW6jKqaAJg6u59tJTnM6WxC2YRD4y8B3gG3sot3FL6Nh5efDKsuThsvAoRERERkQEdJMGtc+6cAbbvMrNPAHeEVW/DZ3fHNQW3Y2TBzMfYNcN/ogvDEEptFHM2d3FK6b2Z5rtVs31QFmUyP9vxJQAaC6upat1N0XY47ekH4mDVFxiP1NZK3J82WtfBS6cCasdnN9vD9mjmnXLYe3w+U5q62Fw9I5MdbaM4E4Cm81KkutOZfrJ5pNlOdSbAjfZLkWYtS2miio6w79rGpQC8uL4U2uHRycuYfPxOykp9IFvFLmrYQQcF7KeEMloyTZmjMkrYD8ALYX0hnTRSA/jpbqPmywDpygbaKKa7tpnKHe2Et+MbSGyGwxt28o+1X+W8I0/GD8wlIiIiIvKytCqxPHusKjGcFNyKiIiIiIgMh8H0uc1uLDz6ChLLI9sDeJQouB0jr+YxinkG8BnNNko4i18yj6copINXbXva77gD32S2Gp9R9YlcjsgLnW+jzGsr9Jg9J7k+sic8KvFfpj2JbXnhHHnAEmgP/XLvL11GBc1sqfY3c6KmwRU0M5UmOilkfypFY6qGNooBuIM3Ar4ZcgXNLGQjAFuYTRvFdFDIforpJkVVjX9DO+eUwD6DZ2HfA9PYsML3RZ9duIUmpjKbLXTSSQO1lISmzlFf3hLaMlMJQTJRnc5MRRQ1S44yzyXsh/L2+BsQ9Te+GWZ+ZJsfwKvCZ5Vp/hUiIiIiIgMaTLPknuHkdLP+J751zj07tEr1aXliedNwFGhmv8HPVVqGHyVoI3A38B3n3AvDcY7+KLgdIx/lGmaGCHTatn3QCs8fWc70x/f4oDOK0JoSz9GUPBA3Q04GtMlgtQM/Nlqyv215OG5vYl10Zyma0qcWNtfOyASqG1lIijSFdFBBc6a59D7KKGE/TzIPgAZewY7QFPjqX15J0TI/ktWs8nq6w0mOYgPdpNhBDRU0U0UTr+YxAB6bDftbi9nHNGiHPQ9M9+dfUkBFuR8wal+oagO1gA+0Z7KNKpoopDNzyfyYzCmaw3tooSwTCDdTQQXNflCqPOJ+yXvwAX85NHMIhy/fxF/b5zMyZgJz8H+csrBuNX7asf0jdE4RERERGXGDmee2Z+b2Dzkc0X/0OwhmNgk/6k9kuObBPDWxPA0fQC8H/tHMLnbO3T5M5+mVgtsxMv2FZqZFDQFC4Dp9c4hOkwFpckqeduK/WPY8tMnmDx3EQXBHYt/kly1FzzlfD4PnTyhnO3VsYFGmX6vfNU03KVooywSWzVTQQSEltHEdl1FAB5tfe7Q/YA20v60S9sETdx8Cy/z3cPPio+E8OPKUP5AiTRktVIc3uJS1tJUW89DM19HeXAbP+pGX25+vpHPyfhpStZTREoLjagAqeIFOCqmlgTQpOqgCoIXJ5CWmHUqHukdKaKONEnbXtVH5RBg+Oboum2Fp6zr+ufRfuLToxnDdiznwoPM14XkvTL7If+3rSYzifAysX+UrwExgAz3vQoiIiIjIQS9F7s2Sc91vZHwMOC4s/9w598gBlrcBuA1Yh0+x5QPzgAuBNwAVwK1mdq5zbsSaRSq4HSPWjJ/iB/zgTu34uGYuPiCNZoCKBnqK5p+NgqG8xDrC+uivGc1BG2V1pyT2qcZncFPQtRjyw2DdT58wnSamchdnsYsq1uGb5NaxnQ4KKaOFJ3llZnCo/ZTw8NaT4HPmGxp8Dljzm3CiJ+CWFnwmEnggTLP1yOUAPL7vWIpW7GZheTFlIR9bSwNV7OKo8g3sKp9KQ4Wfc6iwqJPmXRVsq5nJHLZmmjWDHzhqIRspCDnbvBCQ54UJg6poooUySmjrMTVRRxh9ee7q9vgmQAOZ5V+XvsEH8TPDtvUHEtyeAOTD9BX+5ReBxTDtmGcooY2//jZkh58HtqyA5hWwBlhzFPEURA/iM7z7Ew8REREROegMpllyz8ztscSTfo4oM1sOfCW83AF84ACLvMY5d1Uv69cCPzSz9wPfxkco15nZHOfciPygVXA7Vp6B0H3Vz2m7GR+MbsYHvVG2tTDxXEjPKXqiaXsI6wt72VYKIaHpM7XlQDXcPusNPMIxbDhhUSi+k7Us9RnY24+GJT6jue7Z5dAO5cc/z55rppNpG/xAeHQDrIRP1vfzZp/zT+1fh28tgG+dQPsZlTz6uWUw3Y8YvWD2Y1TQTAO1vJInqSj3TfJbKKOippkCOkK2uIBUeNMtlFFNI1NpIk2KfSE720YJBXRQQht1bKeMFuY+GLoozCW+mfA7fGvg6JqV+2v1pk338OT8V8J50d/nBOJR0nM1BfgwsB/WT2H50XeH69xBHdspoJMU3cw75WuZ97KdOloo435O5K+r58M+38ybVUvha3vxQW49hObWvvP0lkHW62A3Bd9ypRuIbupVAn8P3BRez0xsewtwFHAt/mZKcmLn/PDcNXLVFREREUkaTLPk7h6vnh+BPrUvYWavAn6Or2UH8A7nXOOBlOmcax5g+3fMbAlwGT4ieQvw4wM5Z18mjUShIiIiIiIiE07eIB+jyMxmAfcAh+BTY+9yzq3u/6hh853E8vI+9zpAytyOlVuJmxjvwWddi8Lz5MR+UX/a6AuQvMMTZWh7a69fBSwGNxd2VfoCN7CITgr4Ie/hjtZzWVi6kXW3hs/WFfisbDv4Qc2iz/kiYB172Ass5cDmft2Lzzw2wd0nwCOz4AzfH/eJ7tfAJ+HIY/5AGS3UhL64UbY2jzSN1HAIzRSHZrkFdGQGjyqmjVfQAPgRkUto45U8yeHbd/omx1Evgp/gs9l5wD3wbGiWXY/PGR51CtAIC+dv9CMmAyw5Bh65F3/Bc80CHgeP5zPtVX/jfL6dGcl5CY9QQXMY4TmdmX8X/AjaL1DB63iQtcuXctOe8wGoPftvFP57B0/8+EzYRZw9/9wfw0I+PmN5QDfdRlhoms4UfOb5UXzH490QRtdmKnAxcCfh2ofRqh8BlgBnnO+bwC8Gvhi2LcGPzTXjA36/6L7hLmDLZnxmey2+eXiU8b4Xnw1+TahX1CrmQXxWeB3x33lmeK4f4vsWERGRCWUwUwGNYprRzGrxP4JqAQf8nXPu56NXgzB9indon3sdIHPOjVTZw8bMcq3kaufcigHKOgN4H74D9TRgJ/7X7Hedc3cfSD0HYmYzgO0A24+HGckmx9FIxtFytC0a0TcPH3iWJgqM+osWAXXEIywDzIW7T1jORhZmRjT+7pMf8d28Pwd034rv9x2avrKb+Af9FOIf/JWMTNBUGc5zYXidB6yDc5bCCjjiE3/J7FlIB7uYSgltFIRmvQA17ODN/JxFbKCYNpqYCvgRnsH34z35wYd9zPLrUFgH/mZCK2zb5jeBbyE+BzhtMbjfwiOVR3I2vwRg5+WHwXW/Av5M3/1dDwXO9IvLZsANXZx5+B0U0MEH+R9qQ+DdRgkdFGSC8kgqMfhVNN1SFPiuZzFPMo+tzOae505n7qFPhvf3N1bfdAa8cy3+xsE64gDuYBiMqjg83gIzZvhVzwPLgDPwgehlDm7wNzgmXdFKXc12/pOPsIoVrA3BbS0NtFBGI366qaWsy0wHVUIbL1DBVJpoo4Rrt37Mn6eoAx4oIv/UvXRdMcXfEJgZqnUqvkn9LcB84oHv54Q6rd+M/y7kE//b+2NgAfAE/jMQ9SlQk2cREZHRsRf4RvSibjSa8Oaqx2/802BG8QAHBM/uh7rfZF6O2Hsys6n4zNXCsOpDzrn/Holz9VOHUuIUzV3OubNH4jwTJnNrfuKob+MD26RDgTcDbzaz7wJXuNGI+JN3daLANcrkNhEHsemwHAW4yT634DO0c4FCuGD59wGYx5N8qemzdL19is+ErQ/7fq4N/49CHnGQ1lvgmgyMRiobuDs8vtRz9Z1l8PhCnl72Kv96Ez7oeBxYDNOXPp3ZNRUGjmqhjAqaqcD3061jO81U+CB4DT6QCRnaZ8Po03vxYUrUQ3M/cZhva2D9WYszAenOiw6D9WfCI2fi+30mr8lu4AMwuQa+FVbNhyMPX08VTXySfw/TFPmhsaPANqp//F66ySOd6YubRzoT/B7DIyxiAy2Ucfqhv84E7ynScD6snnGGz1p+dClxsPXN/i//sDkU36e6Ev9BjDL7p0LRMp+NPQ+4yK+dvvRpTuAhuklxKr+lgmZOv9LfedjKbGazlYo9+zix/PeZ6zRt2z52zyqibE87+bvx34VolO80/G7+a6mgmWYqOH+275fbTAVzzt/CLzmLH938HlKkWcQGANZyHM1vPYT9XyymqrSJJSGt30BtZhTwp296lc8eh2Edpp1yOjt/cBjcucwHxZnPwLPE/bHzUbArIiIywQ2mz+0ojJZsZuX4NE8U2H56tAPbYGFiuWGkTjLeMrfXAv/Tz66tzrltvW0wsy8B/xRePgZcDWwFZgOfAl4dtn3JOfe5A65073WI7+q8CWZEv4OjL0EVPhubIg5ukyMclxJna6uBxfD83HJaKOMzfJlb54YIIo+QjdqLv2QLwkGPjsTbGiFRqPkWoB6mLvUZv3YyM3ItX343Z/FLFrKR1/FQZvqip5hHB4WclL6PKU90wSfIBLe/aojDj0bigaTnAMfMBc6CTdcczk2czy84F4BH/7LMt0p9BH9TIbqntrIJKqr83+giOOLffbb5RH7PG7mjR9PjwpBWL2Y/Kbo5hGYK6KQzBL1tlGT2K6YtE9iBD3y3U0chneyiKjPFUTU72EE1P+fNNFHFPX95U3zTY3EbcdD1xCCuexSgJQO1OYkrBf5WQDS10RTgN8CFMNMgJGjPuf9mtjKbt3ILh7E9MxXTKdxLC2UU0slM6ilr3UfR44miV+PH7EuOBD6Lni0YIJ4iK2qxcBhxqwcSz08ArfDcmyszNwWqaWQjCymjhVWclDn1TOpZwiPcwts4kfu5l1MS77iEFaxiFSv4BW+kqdGP0vbivaW+5fNXgPZt+C/eonDUHRzcTcVFRETGk3GSuT0bZpQMcEDwbBvU/TLzctjfk5mV4PvYnhBWjVick0NdrgMuDS/f7Zy7YUTOM86C2y/0Mcz0QMfPwf/MzcOHKK9PDj8d/vCr8T34uoH5zrmtB1rvXurRe3BbiQ+QSon70c4N20rxP9TbgWXws/m+6etGFrKdOgCeZB6rLzkDVq4MB9UPd9XH0Ez8P2an4qP/Gt8/GGAJLL/0bt7MzzmXX2Qyoet5NS2UcSK/5/DVO+Eu2Ha1P+RR4pz1DAgz5sLCML20+4hvknwXZ2Xm9K1nZmaO38Y91eTl+fO8orSBzU8ezXHzVvNkxzxmFtYDcBnXUcd2OimgjRJKaKM73JrrpJDZbGEqTZk+wwBtFNPEVDopoJtUZsol8FMbtVBGMxV0k6IkvIM5YaTkFOlM8/MoIGumgkf/uMx/otmGHxTPl+YD10biADZqO9OND8xqgM1wfLhZsgnfz/WdwE+BcxJ/njn47OzMdoomt7G0fF3mGlzUcKv/06WI+zwvIc66bvenybwO8z33aJYP/vsQBbWFoZrJofMrw3MRcWOXqPFB1J99B3HAuwA4GVwV2AZ8YBxdmkYyN5p21xVRud6PGv704umkSFPPLDawKHOz4j5OookqnmQezR0V7NkyPZOlZgZwZ3Sj4EvEQW89PUd1FhERkYEpuB1knQrwd9rfEFb9p3Puo0Mo52Lg+vDyJbGYmS0C9jvn+pzCIzEVEPh2cXOcc6197X8gJkqz5I8Rv9cPZ8+r5JxrM7MPAw+H/T6Kn8dl5BRAJmnUDdTAzRedwwpWsYNqruMyAD7KN9hOHT/iPZTQxg/T7wGgItXM0//xKt8t/O6r8O1vX47qw/P/xauirwansvqKMzix6/c8xAksDP3US2ijhTKeYh6Ht+6E8jgJmI9fLsbHODV1YcMSYKkffGsjC2mhLNMsuIomqmjyc/6WF2QypwDz5j1FBc28orAhE5CmQ4PpAjozQWnSjpCVPoTmRF/bPNoopjNEdfspSTRb9tncZirII53pbwpQQCcltLGQjSxiA4v4MwD383pqjtnBvbtOpeuWWbDq4/6An+7FR3BhvtzFS+MBzD4K3AtHXvsHHv/CRT5TDpAH711+LXdxNpe8/3q2MpsXwns6nV9zF2dzPj9lHk+xmMcAqPxke9ycvhw/9RX4QLkVH5C2Eg9CH4mOSWZho0xuOuxbShj4LBzfGta3J8qJLl07cfAcNYB5BngMLA9/dyM6/wJ8E/4NwCyoTLdnxrQ64ubnIQ8On7+T5azL1O3y2ht8uUvgn6o/z5tf9XPWP7YY8DegrvnxZ6h853PsfudnYVU4zy6A75GZIktERERePgYzCvLIRmI/IQ5sfwd838yO7Gf/TufcU0M4zzH4uWvvw8/VuIE4ZTEff9v/tLBvGnj/SAW2MAGC29DX9k3h5SbnXK9RoHNujZk9CcwDzjOzvx+VvrciIiIiIvLycPD0uX1LYvlkCBmQvv2VePjNwUrhm1me2s8+TcClzrlfDPEcOXnZN0s2syPwfWsBvuOcu6Kffb9DPODUEX313x2qHk0WroJ1V/omxi2UsZ7FXPPrz7Dg9EdpTNew++4wSuun8U0/v4hvFvp4NIp2DaM3aNBBrOIq+Bq899JrWYKfGqeWBtKkqGM7x2//E3yEzLDIv9jhG+KWAGfWEWcnZwEfhNW1x3EH55ImlWn2XUAnBXRQGLKkUUa1jBbKaMk0WY4GJioI/UnTpGiglk4KMlngFGmqaaSE/dQk+mN2k8qM9gzQSUFmyqOoCWw0enJ0/loaMvUq62ghnZfihZTPqG5lDm0Us5albGUO9eHfqtls5YbLL/cZzSX45sbT/Tk/M++fqaWBJqoooyWTiV7KWjaykIVspIJmDt2xO9NQwC0Dux7fJDj0bwV8tjX6B3sycR/y6B/8qA951Azfv9Ge26JjWvH/HELc97Yosa0Rfx8w+Z9JMpsbZXuj9P3usO/uUG5ykLbuxP5Rc2eIRzOnl/XRoG9T8D33Q5cCVwrbKqdzC29jDlv5NF8GYPMPjoargPomfDMEDUIlIiIysHHSLPntMKN0gAOCZ1uh7ubMy+FuljzYIO+vzrmZvZRzMf03S67Gd1p7Lf6XUA2+L6Hhf239CT+Z40rn3IhP5zHeMrdvN7N34XvJdePbbD+Ev1j39XHMgsTypj726W37AnxnxRFxwqX38sztYcCaIuCMrwK38gQbgM8mTr0aHt8PtxXjf1WPp0GhRkHzA/DRZfyg4gPw1msBmEcZdWynmka6pkD+IjJ9Pg/Ff8sqga69kB/10SwHV+QHb2qkJjPQU6STQkrYTxVNmaAv2icVmgpHwWe0PQpCU4mRj8tooYT9FNCRGe0ZYDIdlNFCE1NpC02So2NaKKOYNspoIUU6Myp0Gfsoa/VvoKgV4EWKC3cCcFh6J7sqJzOLehqp4decDviRpLd/r47vcTm38DZezWMsCM259yfOW0AHR2wOQwW3wqsanvaf02fw/WRDQGp34APIVvw3MvkvSrScPYVVNOVV9JwcGTw5IFTU77Yb/09kNKdzNbHucHw6rI/O2UTc7DnaLwqiq4gD4lLiwakg7tMb1TP5fqJzJRvSZDeqSfyLYdVwxPbn+dSSb8G5UFvr20UXvLeTt7z3Z/z1M/PhK8uJv9MHez/cfHqOtC4iIiIvcZA0S3bO2TCVsxJY2c/2HcD/hseYG2/B7cKs13PC4z1mdhtwsXNuT9Y+dYnlge6GbO/juJyEuzb9mR4tPFO3Grg/vIoG9tkQXmdNj5NRP9gqTQD3wr5D4UOz+MHdHwDgzO/9jHP5BQ3UMnVyE/m1XbDc7z0ljMvWDeRPIQ5OWmFL5QyeYGEmUxopDIFomlQY0zieY7WDAlKkqWIXNSFK8lnPF2jmkEwZVSH1WE0jnRTSSSEdFGT62FbTmBlcqoXJmfUABaHPbYp0JjgGKEzH9XR5YK2QH336d8A09jFtzz5eddjTnNz9cOaNf6jq+1AIn8m7xgerIaDsqob8DthdXcQhu9vjWKshXKcog5rs3xoFjR3EQai/aPG6WnoOADUFHyiXJ8qAOKAtp2egHI0iHgXJ5cSiADWqa5RVbQ/HJ8uJLumesD3KukbBclTf1rBvmp7zTUdlJAPaZPCc3X94W1i/AXgMjp/8J7/+DVBfuYA1Xz6aX375LL64+t/8+hX4TPr6jfjO9AdTsFuMvxFbhu8rfDDVTURE5CBy8DRLnpDGS3DbBvwC+C0+u7oPmIYPWa7A//w9D7jdzE5zziXb+ZUllvfRv+TP1sl97tW37QPvEmkHoqHU1CzxwPwAnp8C1/lBk3415y00/mM1n+RrvLo7TPIbmrHOrYY/hkzdtu0wKwq6LoO5O55lbfV+VrCK/RTTESKbaCTjEtrCPLRxpFbCfsrYR5oU1aGZcSGdVNBMC2VMZReL2EDtbh8MbKucTj2zaKaCJqoyU+QUM5tDwjHZ0uTRSSE17KCEtkxGty1VAkVtpLrTtBSlKKMLiwLFKAiLgquiRIHbEvtEAR4+sAWofKbdB2nRAEzdxJnQ7Exlcsqe5FQ9k4mD0KgpMYl1yX2nJPZLZnKjQLE7rI8CzWSgHP2nMIWezZWr8RnZZHDanTguyvYmFSW2RXVLZp+LeOl/Qtn7Zf8L04GPAwuJr+fTQDks/Y8/8evK07l0uZ8g+fubPuT/dVu1EK6JgseZ4flRfLvvscya7se3fegmMyCZiIiI9HSQZG4nqvFySQ91zjX3sv43ZvZN/Mhcr8YHux8A/iuxT/JnfecA50nmXYr73GtYdA+8iwzCXnxHRuDTJ/Bo+2lccN4ybjz6PGrev4OT20Pmcj3U7IjzTntDX84pDcBmuIhb+VP1XJo5hDJaAFjLccyinhbKmM1WWsJ9j7wwh20FzcxmC4ft9k2CLWQND9+z0wdK28gEhUdUPk9qSZp9lPECFXSHBgI17KAzZIGjKYJS4TOSl8gaF9CZCW4BulMpUt0+SttVOZmywtBMuRD/JpNNc6FnJjOPngFbKfG0O1FABvHFigLZfcS3cVKJY4oSx6QSZZfS81ZRKmvfSFRGX82bk3VIfn2iO6TJADbKyLbiA+oO4sA8Hc4TvU42f44ywVEwG22LsrrQ81+U6L1EdU5mdqNmzO30DKTDFEh2Clx55NXwz371xfOuZ/u8Ora/qY4N3ziKGz58eTyFUtEMWLUALp4bGgZFYzFErT1G+gZZFMzm4ec4LgaiEf9HvPuMiIjI+DGJ3DOyk0ayIhPTuAhu+whso22NZvY2fFqjAD+FTzK4TU4Q0rMj5Uslf24PJS0xUFPm6cAfhlCuiIiIiIgc7JS5HVMvi0vqnHvazH4DnA3MMbNa51zUCLAlsetATY2Tw94M1IS5t3r026fXz0okI+9BuKoerrqcC1bdxsrl58NZIXO7HfIf8R/8FmBGlIW7A5/hexyOPmEzXXXQUu431tBIGyVhwKeqzCjHjdSwiA2UpVuY0tgVZzj34LO11fhbLtuJb5sU4ufebdgJxwOFfjCh1qpJNBZWU8a+zMjGSWlSvg7pFgrafZYunTeJlsIy0oV5mWxyujQMXJXXQunuF31/127i5Fo3PQdkgp79ZCH+5CcTcg30bF4bZTGjrGQhcRYTejZJTjZLLg379da0ODtDG61P89L+s5HoG91bFjqqR/YgUHXEbTSiptuE9xf1uY369ib/FYgGm4oGw0rWNZqbNzo2Wt9BPKdvcuCsPfim3uuBK/3qZW94FOY+6tcfAZd88/rM3M0f5+v85C9/5+fKnQkseaM/6JwF+P65exmdZsKV+L63c/FeDYAAACAASURBVImzxiIiIpKhPrdj6mUR3AYb8cEt+I5hUXCbDDgHGvApmXkdRP9ZOfg8B3wPVtRw8W03Uf+mmQBceczV1JwLxfdAfQc8GwZgKlkNNbX43+77IP/VUFkXkv5F7VC4m65CmL57TyZQmt/917jZazvxJy6almY98WBMyeatdxAHiSE4K+1+kSNOeB7yYFr341AOm2YdDvjBqKZtD1HWI0A0/XbRi1DdQnNhRThtHh2hcUJF94v+vURNhmvCMcl/cJNT8ETyiIOvZ8K61vA6GmG4iZ6iZsdV+CmVIu3hvLX0/Mc7TRwQRueEOBiMAtns5r9RfZN9a5MBcFQGxINThdeuKDQXj0SDYO1JvP89+L/J3FCHRnpO+7ODuOl2sqxIMqgllBs1bU4G89G/MmGQrK67/Mv81dDVCvkX+TJObngYLvHbfnDypXzrVR+kcko7d9adzFf5NAAP3HAarJkLu4Cfgv/jrAsnWNtLJYdDF36qvDBdmQa6ExERiaXIPWhVcDvsXk7BbV9p0Y2J5fkDlJHc/sSBVUfG3nNAN5wHV53xVQCu/MHV8DhM2QRzGuC5EKjmpfDDlS3xh7AJOCEUE+ZcjQZcygR3yb6c2dlB8P9g1RLPtQo+QGrEB1KTiQdTmhzKrcUHiYUwf9tf/bZoVOcG/Hy9IRhiMZQueZG2pZ2ZaYiiUZYbS6dR272Tltoi8tJpdqV8eraCZvaHfTsooLb1edpKfaSYJo9pDfviOWKjIC0K+vaG5T3E/3JUA0fgA8K5xMFgbXiOgrpkv9YoOIzKSAaxIbPcVQh54ViLbg7sgL0L8ulOpTIjS0faKA4TK/n1NelGWlJlNFNBdRjFuqTDj3LdWFhNJ4WU0UIbJdS1xve/irYRB7DziW9YtOInIItuYkwhlpxbNxl4R+87+/NRGspaA2yG/MQcwF3dsP86mFKO/3x9wm/KPx4qa9vh9XBq5e+4o9RnbpdeuJaGC2u5l1PZ+dHD4LoquMzPn81FZ/pbe+034f85iyqdax/ZKVn75uPng7ewXJ9jOSIiIhOImiWPqZfTJU1OE9SQWN4WXteSmRCmT68Pz8+hX24vE3uBa2G6nybozuqTOeeE38EzULId5oY7Zl1/hD/vAO6Co6rxQW4YaJlWfNB2BPGcrhBP9VJFz0GOihLLUVPdjqzXUXPYKHZoxTeBbcW3GSglvtWyDx8Ur8c3nY0+3bV+/4o9+0iXp2igtsfcu/vKy+gmRVNqamZwrGh6ohTd5JH+/+ydfXxcVZ3/37d3npNppkmahDSBlKa0ja2UUmiFSiuURxFFUdTF3f4UdVlx1ZXfyrrsb1FwZXdxV3dxRcUVFVdcQRAQxC1YpDwU2lpstw+0pSkJIUmTkHQmk5nJTO7vj+85994JbZOWpE3a83695jX3nnvvOefemeTO536faC45xU1QFSVNZSSFVY7UstXjZNWc8oiwPRkpWQOidWzYt1xM0Hr8PDbV/fvYXXIqIXLu+AVsqvp6CBTA6oN9M0vdWr29JWUkiRMjTR6bsMr/1lZei02e3qppSsDabi3hNDHaOIkCAcKqVjDAZnsBcZJkCdFBNdV00BauVacTIkCBKAPY5OkuEeF/Em10z690k3edRBvhCi8HXUnnkHx+s4ddG/0gYKZa1g8/hov2wrD1xch3R/fVCTEtrIcnztqsrv3LEFkH381+Xto/DZTB81Wnk1wSp2pJB//KFwD4+fqrydxQDvmrReSuXqs6ewapO6Qzh+VBudrLv71FiHU2iPcF7VTtSYhMhcw2igsEGwwGg8FgAIxb8jHGchznWM/hLWNZ1qmIrS0IvOI4zqxh2/8DyaIM8A7HcZ4/QB9LARWYyX84jvOZcZhnHa678xcoNv8Yxhd9rVfyeefX/FPflwnegBd/uhnogfQmseYGgGplUYvNQ4SLjvnUVkl/vGkEz2qp67T64zm1sNEldbK8+Z+fP07VRkQkeG6tWnBqHVIr+wz+tVg5/1g+u6iUUIACSeJkCbm1cXUZIy1uQ6riLoi4ndnTjrUZeAovU+8WNV8dv3sNrpvzvmtKCReyPGufQz0t/IEz1JSzbiZpm7wrRgMUqKcFmwJplZC8m0pA6vnmCNNNBXls91zC5MgSIkyObiqIMkAv4oqdJkpYZZC2VfZqgEq6SBOjgE2UNDEGCKnzTPmuUZQBErwB4FpzZS4iyLVYr6CbmKo1nOjvI1kiYj6WTRPKDBE8kM4L4Ll0+78L+kGBtsbr2sTaKq5dv4eLYx3rW4v3vbERgXyues/gfqefXPwO0kR5gpV8c93fwIPqmAwSoBFBEh5vwXuQ07UWIsskHnwNeOJ2pxwUUB98fg/yLxfGz/3ZYDAYDAY/+4F/1Sv1I+W6OZr4f+O3fBHqykY4QNHaB/XfcFcn1DlNVia8uLUs6z3AY47jHLB2jmVZ1XilgAC+6DjOvwzb5zTgf5GfhOuB8xzHGfBtjwK/x3NKbXIcZ+c4nIsRt8eMoHpfAJdfATdC+dLX+IX9QQDOf/Q5+WS2weDdEAxDqxIsURsqahFBV8Wbc2JXUyxG/WJGW2z1e0a160RI/id2w0vZ6G1a5GQQa7EWQ1ogLwauAMKw/ww5zzfsBK9T61pAdSxugAIJel0RWMCmgi4A4qSYvj4lOuar0KG0S8CGinmI4Pkg7LuolN3I86MdzGE9Z1IgUFSiaBa7yBHGpkAlXUSVuI6RpooOwuToJVEkcgeIuaI1j+26T+eVu3EBmzhJutwMWEKKOFnC5AgRUtZem7yqQZykgE2InDtOnGSRsI/6EjHlCLn7a0GuiZEmTpI8NtPUtihp4qRIEyXGAIm+lOdO7be8andn8NzetbDVngDa/boHz5Vdf9bgidvhMcda6GpXZm3xPxmogL1zp/M5vsViNgCwiYW0UE8H1TTQzFN7LyBYKtdgsHWqiN0G1ceH1XsjYv2tRGK+NwHr9f33vzHlgAwGg8Ew/kwScfulwxS3/+iuTqhzmqxMBrfkfweClmXdj1hWm5G0oJWIb92f4+V7XQt8e3gHjuO8bFnW7cCNiBR4xrKsfwR2A7OAL+GJ438eD2FrMBgMBoPBYDAYjnNMndtjymSw3DYDp4xi1/uBaw9WE9eyrCnA94GPH6KPHwCfchxn6HDnORqM5Xai0AhcA7fD5V/8BQBLWEcDzVyz5X6xfn0Nz5U4gzwS0W6kmtnqvQp5mKizGHerdX92Yb/1dR6e1TXi60Nbe1/FyxoMYr3LIpa7rG8O3UjYYzekeyC2GDhfbSsAH5R+26vKXMuttuIWsMkRJqRsngAzWnqkzy9C91OwX51/DKi+Evp/NoVd4VkkifM4FwNiNU0Tc12KtdtzmCylykJaRYfrFq1dewvK7XgPDe74vSRca2qSuNsX6IRR3rM4beEFcWcuEMAm7ybUCpFVtl55hV3Tubgi63kMt/ZKLK4kngpQIKv601ZcbRHWVmrdd4gctbSRJuazhIuLc6BQIJoaJOgvx/Sq+rz3+D5n/blrl2Xt9g7F2Zn9Bcug2HLrL1MUQL5X3cDFuB4Hv6ldzlaaaKCZrTQRUtcb4AWWkCXMVprIZUP07aqRg+4DLoHI3B4yq8tlPjq4YzWwvRW4C8mg/BoGg8FgMIw9k8Ry+3dQlxjhAEVrL9Tf4q5OqHOarEwGcbscSQT1DiSlTyWiClPIl+hZ4EeO4zx30E6K+7sM+BRwluqrC3gR+K7jOI+N+QkUj23E7YShEVgBl6vqUDXADfDXc75CLW30kqBDBZamibKSJ6iki3paXJfeNDGWb3+BtXMXUU+LK7gas7speX5I3Ht1nVWNTkakS8To2rg69rcfLy5Xu7TuV8s6UZGO79wmx+/ZJDV7A0CT7mc5sAS4QMYfVO0DpeK2nLXDrhhM9Ivva2Qn8A3gSdjQ5g1z6RJp/+W5l9JNRVFcb5oY3VS4glULRfDceHXMK4gYTFJKjjB/YCE5wkXuzDp2FigS5Eni7n4hcm4srMZ2haxcNL+w1XG3frRbsharer5pYsRIEyXt7qPnpUV0jlCRy7K/n7ivrHaCXjc5VX2hhbwt8w8UCoQyg9h5CG7xfa7aPVnXFO6muEwSvDkDo3Z99tf59YtbXVN4Kl5M9wJwZkOyLMgbdoJOqtml3Mx300iSOLuZRRu17metv/fdVJCgl24q2LZ3gfTXGpT/oingVrxQXPYgweybkQRVBoPBYDC8FSaJuP3KYYrbv3dXJ9Q5TVYmvLg9njDidqIRxPvRPQ84V8TuI/th4VTO/8MjADz508spu6qdvtZqSFl88fRbARFfzcwkTZQX+pfwsZKfAHAfV3EbN9JLgoVsIk2UmSr5doJe1rOYBvYQY8AVaSfRRoEAib4UwR4gC44SpJa20IaRb48WQTuBH0Jri/jp78fLaTVzLrAAsSYvxkuCVQaZKshFgsT7BsmGIaJV7F3Ab+GP68UYN081X/pN2Pi5eTzLOa5Y06IvSbxI8GphGieJTUG9ewKymQayhOklQSdVKuGVWEf9wtZvadVtfhE8fBuIOPWTV/sHitIUUyR6tfUWcLMnT6OXEDlXzPr71gLa3+dw6/Dwa6CXtfCtV/8CQuSIZdOUdCtHkRbEIlqCxLT643H9Vl8tWuWiFJdZGi5+h4tdTRWS8bpM2nfWy0OeDqp4lnNIEudl5rgPbCroLnp48Lr7hZJtSeI83n0xg2umehbdduAe5Du4ZSeSvapZbezAYDAYDIbDY5KI21ugbtrojmt9A+r/zl2dUOc0WTHi9ihixO1k4D3Awwdon4GEeO9CVCXAX8rbYiCBL+NsByyrhhUw/ZZXmckeTuNlAAaIsoDNVNFBnFSRIEoSJ06SFuqpoJsz+AMg1tGm7FZKdg6J8NHDZxDx8Dx0tEA0DHFlJbYiiKioRYSMrtlbgSdyShDRnFLr34Wdj0p2th7gZq1fHoevz/+8KwZt8q5QSxJngJjrRqxFoLbWSi3ZqOsu3EYtvSTcl04WBcXiVbsV62vjf/eL4OGWWt1H/gBCWDO8Rq5/XG/+A0XrWUJU00mILAMqC7Qf7d7tvwZa8GsX6Col6GIMFLk5VyrTbR6bGANMb0mJ9bYEr9r2HkSo6rrK/mzJxSfjWXnBc1PWrs4anZQqjHw/1GedWQw7SmbzNOexg9Pc0lEJ3nDnXCBAjPSbHmgUsIsedGQJ0UI9G3+9TNL43Qts1w+Tfo78LRkMBoPBMFomibj9+mGK279xVyfUOU1WjLg9ihhxezwQBV923QPTgFimvgQ4wGbYJS6cZ8/6PVnCStp5gmEmzXRRwevUUkG3W3oGJPNwjAGSxJnDDhb0bwEg0oaXPm0nnugFES8liClX11MFETFleO6qKSRPOLDzG3LbOLMWCENms7SvKVnOH1nAbhoBEWulStzmfFbXKGk3flbvB7DVV4I6S5gkpe6xIpVt1UfAFav+fgBXuPqzPuv2LCFfHV1PuA63zGry2KSIk6CXJPGimFst3Pwxu3oc/R4jXSRuc4TIYxMn5QpZ3e4/pko5emvLb0JZiCtVjK4/Xrey0I2dLxDKiFU3mEUsoq8j/za0tV0LVi1g/Rm65ULqi+FlXvbXYwb5jui2MmAevHjRfNZzZtHnoN2se0mQJexmn46pGOYuKuimkmqfVXYrTVTRyWYWkCbKk7+6XDa8T42fAbgbU1bcYDAYDCMzScTtP0Fd+QgHKFp7oP6v3dUJdU6TlcmQLdlgMBgMBoPBYDAYJj42o8+WPNr9DKPGWG6PIsZye6JSjrg1A3d8QNKYBYAaiMyXjFJNZVsJUHBdU0NkaWQ3gFvfVceBnsfTAJzGDk7u2Ye1CXhcDaWtdjobL0i9U12btwR58DkbseR14ll81yOWu8uAPDz1tbMBWMfZPMq76VRZr7TFESQ2No/NTJpdZ2M9Z52QaAenFbke++NPdQInEIuqzlBcwHb7ShN1LbX++Fw/wy20WUIElKt3mhgdfVXutvqyFroLleQyISpKut256KzBBQLESZIjRIVyGc4RopSka0HX7uQ6S7JOtDQ80dQAUfd66WO88XJF807Qq5JZebG/2kJe3b+PZEkp0ztTUodWZ09uwbPY+uvlHgjd3sebk1JpAkA59H9VahM8Gz4HkLjaNFE3uVeLr9hzwbW8265rPeDOvZcEcVLY5OlUSdoe5TJ++933woOI98BaHS+9DUlA1YOpnWswGAyGYiaJ5fZbh2m5/Zy7OqHOabJixO1RxIjbE5lL5a1hCTR3AGlgJqxSm29ymD6rxU3AVOmm0IVa2ly3V78w/FN+TBWd2OQp78yISNWHPYPog1pE6Pqz7Zaode2q6v8HfAVsvGwea1jBQ1wBQCdVvKESC7Xvrae8rpN0SlxSA4EC+bxNLhNmRfXv3ARE1XTSrXyhX6WeXEFEaS4Tor6kxY3RDZGlpU+EUmVZN8lsnNpwmyt0AXds3e9w92QttLWbLEAv0+jtTjDYOhUSg7Am6LnhtgKlSEzyduRhA0jG30YgAeXzX6OnuRYCcuGmn/K6K8L9iav8cb4n0UaMAVfE5rEJkyNKuujzDJFzP+c4ySLhX0E3OeVmnSVMra/2lF62KTC7U937tqnPsRwRva/6Pkv9GevPXm/zt+kkVHq9BxHBNrDU1z4TWAD9y6fQHJakYG2cpK7HAM000KU+b+3KHCPtfo76oY3+jHqZRpI4a1jBczvOl88EpOTQnQ6ssGDNOiS1GZhsywaDwWCYNOL2DqirGOEARWs31F/vrk6oc5qsGHF7FDHi1iBUIzG5DVC5SpoSwO3AfAfWWmLN0ta59znMn7WeGGnWdyymvlq+QgvYzDk8Sy1trGQ1bdRyVqfE45LFS0aUATao5T4kgVAFXnkhnRZ5KXyn/s/4CX/Kcy+df+CghYx6adGTws3QW7p0H+mUlEmqqu4gVwgTs9OkCzFXDGdSMaYERLQOpWKQtyDiEy4pVVYmgSdGI4MESwewA3kSZb309iUIR0RAhcI5CgWbQt6mr7nGu2ZaE6WQrL3tvutZp9pTiMit0+Oo86pUyxFgrtrWCyrkWAio/5uRLMFIjoqKLqrVgwZ/PLBNgVra3HJBIIJcW+L9CaiGlzLKYzNNWYIlXVWIBpoJkaVaBd3GSDOr/xUiPXjxtpt989SW+QyeVV9bcPX+/hq6/Xi1dvt8n8FcJHZbfyfmI9m4gZdmzmaAmBtF3sxMANfSG1ID+2OYdcbtNmppoZ5HucxtTxKn9f7ZcD3QvlYNqK25RuQaDAbDicskEbd3Hqa4/XN3dUKd02TFiNujiBG3hjfTIG+Nq0Q81CD5dUqBVvW3eZ9VtCs1So3kbQgU+MCM+8hj824edd12V3MBmziDL3EblXTTxFZAXF9/x7uIkaaBZjZwJutZDMD3/vFzMu7zwOV4Fk2/1Xe4pU+LxHbVro2sEVwrKL1AjRIlvUFZ13VZu3yXQgupXrVc49uWGIRAgSmBAkN5G1Jq58ggZFSfGcS1Ws9ruxqnWW3z14wtHXYeGu0yPlcd515z1d6o5qznmtCvDKQilDe+RiEvllw7UCBhSxKmOexwE2dpy7suBaUFXxUdJIm77XlsBlRNZS1qtav1QpWa219LuJpOGntasV5RczsZr6byq2oZdR7bVFuB4qo82pqfwRO6IO7qedXnbKSM0FNq22LgI4ANT9WezYDPgt5FJb0k3DrBmgAFKuhWFuqQK4iTxGmmga000UYtr3zlbXLAGmDN3eroZgwGg8FwImLErWFkjLg9ihhxazg078HLxPyMr70RLv+AuIne1I1XQmWJvK3Cs0Y+ojbdDVwDXILENS6V5o888J/8bN7HRZzc+3OYe7Un1DYBfAe4Fm4MepZKbUX1u7Lq8kGteIJXC0zU/r2ISGz1zetyRFhqK2AAEaG637m+9lLVXulAJCuCNuDAJssb5xE1ViUisPUc1+OJ14ivTz031Lq2RKOOT/i2QbFY11bd+XjXRluxtei9R/WDXEYW44npUiXw8wHK6jqIhsVd2R9zrMtDaSGsLbchcm68cpawK5Ar6KKFes5gE3GSzGI3zyJxsvqBhs5krNer6aS6p09EcBuwDs+dXQvaAJJpWX8Nw0An7O+DwTxsK+BK2DPnq/2rgIuAd6sNeXhk/vkUCLju6tqFW1usQ+TcWGOAHcyhhXps8mxgMR0qRndD/5mk7p4ONwKpe4C3q4kDvIbBYDAYTgQmibj9HtRVjnCAorUL6j/lrk6oc5qsmGzJBoPBYDAYDAaDwTAW+B+oj2Zfw5hiLLdHEWO5NRyaKGLmiyKxhcPr6VZz4LjDcuD9avt3VFsP8v3aj2RqblDtmxk5C+37ZQ43zpbVBjwLZi/iFaqtk1vUtlI1dR3rmnlILQQRv9flan27Wl8AnCn1TrVrcgCxdKaAFXguxlch1ttNllikW4FN+hy61Xk/o85T1dStUXPUsbMZPIttjVrX7s/6XOhArm01RILKouv//2iJZbkXz6qsLbP36bnsx7MkdgCL4JKZMs5C3/jqmk1Z3E+sVMXcBgo0hJsBXDfeuGsil7YOqopq+UpCKtuN642RdjM82+QJk6ODaipVFVqQTNKN7KaWNiroZgencbFKtx3O5ii5a0jm2gbcqQY6FbeWcmub+A5ob+YB5Bt79UwY7IEeVU+3+j3AGbDvK6XsZha9JEgrN2v9HiZLnKQ7twq6SBKnk2qyhNzsyi3U8wBXsrljAUMPlsCteLHSz/8R+CUGg8FgON6ZJJbbHx+m5fZP3dUJdU6TFSNujyJG3BomBw3yalzhNS1GBNnziFvug6rd1V47EbmzmtETxK09A4hIVeKycVnxdFa34tUw2ogEYYL3ACDIm0X/DER27UJcvvXf2y68TFqv4bl5x9X6EjWnbb65NSMqtlz1pf2fO9Rcokj2pgM9eFihzlOSQHHtChG4dRQnzqqDSGMPiTJxRfZngi5gkyZGsq+UgErIBbgJvEKRrFtO6lVVpmcava77sq1idUHclQvYzGI39bTQQj1LlItvBd10UkUemzPY5Jb8ufDRtfKf6y65dBv2oHqWK6A/hWp1FQGW1SJZl69HPrZ66F8gJYbS4RibWUCILAUCJHgDgFpeV8WQJEFVVLkr71Z+4P/GX9LGSeQIs/cHKtvXGuCebvWZPIRxUzYYDIbjlUkibu+BuumjO651H9Rf465OqHOarBhxexQx4tYweQgCujasDirVYm8XXorlqYjw0+3jkc1Wi+DhluzxQkeTass3I4w92vmJFZLK68RireOLNQ1ADdQseYUCAQoFG9vWmZfz5Aph0qloUcZpjR0oECtNk814tX4z7eUQkBJTvd0JwhFJ6FRV0kkju6iikxhp0sRccanF7FaaiJOkltfVzDt4Hw+wsv9JIn8Lzk/AUtmSdz4l34wBdTo6h0a5ekVtqDgVsQZfoDYugb0Lp1OZ7aake8iLuy5HYn7LgDC0zywDxNqcIk4emwe5ks0scJOnPfLaeyAThkcsuAOvrFDm54j0TmKyLBsMBsPxwCQRt/ceprj9sLs65udkWdYiJAPLOxHzRBVyU2wDngV+4DjO02M85oeB/4MkyJiG+Mg9DXzbcZznx3KsA45vxO3Rw4hbg8EAn4UVFbAML2sz6n0+YsGt6ZEmJWJt9a4ttZpC3na36fUhLXC7VG3f0kFZVuNMqemnqXor9bSo4kO2axnd+5Kyht6NHKvc0aes6idR2ct77Ic4j6e5jEepuV75H3dC9y/hhYJo0xlqLoNADAjYEAxANALBErVxPihtChfjla1agNxuF1LsSl6i1ucBffDS4tl0q3Tez3IOSeI8zsW89OulnrhNATd0IC7Lg4irvsFgMBgmL5NE3P4C6qpGOEDR2gn1H3RXx/ScLMt6CjhvFLv+BLjWcZzcWxwvAvwCSR96IIaAmx3HueWtjDMSJozZYDAYjirPQOsV4k67DK/kEoiRvAsygWmSIVoRjOQIR7KEI1nSqZiUQ1JocTuYCUl5qExQNmhhuEWtK6E61FVCrDpNlhABCrxOLXu/pkTtXahKO/+DP2P30I0N9PAn/GjVdXT/sJJPrPsvpt/xKgC7Co1UfHOQS0sRy6muozsfnGcgqdYHMrBfubEPtImlt9yGjoc9m/cAYgePA7PLIDhfbTgXuVttAhbA6eyE6p0ANNTvoZNqqungd+9uYTezANj260VwTzXcfR2s3gn89KCfiMFgMBgMY4atXqPdd/zQz5vbENH5NFII0AbeAXxR7fMx5C770bc43g/whO3vgG+psRcAXwZmAV+1LOt1x3HueotjHRRjuT2KGMutwWAQGoFr5E2XFVqIxOLORYRojdT2BU/cAmQzYQZT4jo9JZIjVpomWjLAQH9UhK+23OYDkLEk6VcGN6FVsG4/iyvWEyVNijgvPLHcS951Y7GofTMLgBhUXuo1vQ+4Fr655NM0sZULn1wr7Y8jdXMfB/Kwf4+UEQJoLoiQHeDNpYbjeAK3ST/5DiNJrQBqgXr1DiJ8a4FO+NXCi9iqkorlCHEfV7Hl8bPkQcJtHYh61xhXZYPBYJhcTBLL7UOHabm9wl0da8vtI8CPgfsdxykcYHslctM/TTWdd6QuypZlLcdLiPIwcKV/TDXWBuBk4A3gVMdxeo9krJGYMh6dGgwGg8FgMBgMBsMJh41XDmik1zhabh3HudxxnP8+kLBV27sQ663mqrcw3F+r9wLwF8PHVGN9Sa1OAz7xFsY6JMZyexQxlluDweBRjZgdq731hYjl9nqK43FLB5kSyRFS1ttcRgJWh/I202dIYqgsIdp3z4S8JcekkPhT3YdyS647dyensYMwOdYVltDz/AzQySxabz7M+QOshLmz4Vr4wBfvoUH8mrm98+/YWVXH7G+0wlNAFtLKKNzTLzmNG23YVvCiYXXO6yDiJ6UzL0/VSanKkPhb8PKd9SPXrQRYDPsvEDfsx+2L30Yv+wAAIABJREFU+T3vZCtNPPnE5eKmvWyrOugZTFZlg8FgmGxMEsvt41BXPcIBitYOqL/YXT3q52RZVileAYRfO45zsHjZkfroQvysfuM4zqUH2S8E7EME0LOO45x7ZLM+NCbm1mAwGI4JuoyQfsgVhU3zoPTtEvdaiueynAlilw4QCBTI521X5BbyAcJksSkQY4AppWmGukq8ISKqn1IgIW64tbSRI0w3lfTcO0PiWFu/w+GjK93+FLavhBs6uP+GCqiUmgbfuOom3vGdJ7n4i4/z5Wv/ieAGiEmYLrE7oU5VRlq2GIkAAvF6fgJ27pHVqeqJdjSCPN3O4yWa6vNNZRPiqtwHUzvlPD9Y9QgfrHqEry/8PK9fUEtLfz2pq1Qd5PvmIYmmNh/BeRsMBoPBcAimMHqL7LH3oQ35loeOsI+z8dJEPnWwnRzHyVmW9TxwEXC2ZVlBx3HGPEbIiFuDwWA4ZvRQnMV3G6zdBqmrxRqpy/0uAzuQJxTJFd2FALKE3Xq2FdXdDJRKbdhU63SoHIR8gCmlaeqrxWkkRI44SWwKcDOwqxtPqB4pvvrGXer9ziqeu/NtPLfsfH789J9y2fm/5rbs3wDw3VWf5trCXXTZFfQyzS1FNLOnHWstzP4WsBMGVSWm4ElIjaGweu/Hu42WIJdwDyJ+21S7Kj10Y883WVy+nq+V/C1bfyHidt8XToZvxpD8ziaLssFgMBgmBDWWZR1yh3Gw7C73LW8/6F6HZp5veaQ+tiPiNgDMBrYeevfDx4hbg8FgmFBsg00PSUblu9U97MN1ZFaVk6kDEhnKa7rdvQf6o+RLbKbRS5Yw4RKVeKoyTCFvE4okiZeliCGiN0eIAjZP7b1ASvLsah7j+a/zLZ8La8/llR+8jTsib2PDnywGoIFm8rbN69RSQTdtKjvUnPIdLLxiE01XbGX6P6QI+qvhRZDbYD8iaLXl1p+Rqg038IMeYDtYH4SVi9eyq/xenkU8oO655pNw16WQWqDmayy4BoPBYBgjdDztaPf1eHEURxxa/R4GlmVNAW70Nf33EXZV71seSXy3+JbrMeLWYDAYTgQ2QtdGQLIic++HIDFTLLlLwyR7JRrVDuQp5AMkS+LU8jq1tJFUkarRijTdhUpidpoQOUJI+TqbAh1UwyNBZWV9eBzP4wUgD9deCu+D59acD8BzV0HHxY8QoFBkeU4SJ0SWXhIkvtzL8i+9IN2UIHerMJJn0QZ0paQ+PLfmMjzR24kI4bvAysNllz3q1sadd+ZGtt2+CFbXwX2bca+zW5TIYDAYDIYjRCeUGu2+x44vIC7FAA84jrP+UDsfgrhvOTXCvv2+5dIjHO+QGHFrMBgMBoPBYDAYDGPBkde5PQtoH+vpHAhVuuc2tdoJXPcWuov4lnMj7Jv1LUcPutdbwIhbg8FgmLBon9v/gnv+FtYCl1gMNkoSqsFKoA4GStMkw6XEDmB5zBImRI6WgngN9TTXMn1WCywGrt+P5CUer8zBg0iWKMRRab5qzsCG7GKawluxKWCr8yxgs5m3EybHQv4gcwTPIqutsgGk2IBerkWSaPZTnGgqr17b4JQF+1hR/zsAfscKti1eJIm7iOJlfm4em9M+odE5rw0Gg+EE5cjdktuPRrZky7LeBjygRs8CH3Ic560k38j4loenBhlO2Lc8Lu5SRtwaDAbDhMUnElL3w5YVsKUcGlXIzVXAeui7o4aX77Q5rWSHKxSn0cuAHaN1bwP77j0Z6lQ/Adj34MlwA8C/M/5C5BlgBayvhvVqrGVB+kpr+MPiEE1lWwkpj6YABXKE2EoTXVTwrg+uAWBB/xbCWbDWIy7KbXh3rxQifgNAN+K2DF5yqX4krLYFll25EYD/s/xues9MsDG1DG47F9g4nhfgBKJcvVQqbPYfw7kYDAbDMWICuyVbljUT+C1Sa7YAfMRxnINmOB4lSd/ySK7GvpIOI7owHxFG3BoMBsOkYLN6RWFXgzTdNghcDfODpBZOZ+N905l3ugi1PDZ5bNgehLuAXfph8Azgp9LPUbGwNQMPASsBNe9dwBaIrRhggJhrcU4TI8oAHVQTJc3jSPG/p0veSbwkyYKLNrO08yV6zo1Qvl09KM4DO4FXgJl4yY8zyC00i4hcvQ9wzbfuZ+UvV9O8vIF3NL8IN/+VbLi7Fbk2Jvb28CkH3o5k3jY/LQwGwwnMkVtuxxXLsmqR8ga1gAN83HGcB8aga7+1uQ44VOyuP/lUy0H3eguYO5DBYDBMKgaAbb71r8GWqUAcVn2SbbsWSfNdiK68EeB+vOz8M1Qfu47OdBlAhLRvvPaZcCv0BGZQ/5kW0irsJk6SAaLYFNhNIwl63UMq6KKLSjZXLeBs1tE9VwRojDT2/AI1j/Z5QhbEYnsqYkR8BRG3O70p1PyfPmq+9BJOX4iTfrhbprX2VNj1V8DXxuNCHOcEkc85SvFDfIPBYDixcKaAM0qLrHOU6txallUJ/A9yZwT4rOM4Px6j7v0Zj+eOsK/enmecfogYcWswGAyTnv3y2vQY7r/1D29GEhiWU1zqpvkozw1EYfoFbh1kJFvzS+uWcvoSqfnTTQW9JOglQRWdUosXCJOlm0oGSNPGSbxKPbVKxcYYIE6S9y98TKy22uHpVUTslqu2PJ63bB84Dyk35/fDZ5b8BwA/33k1Wy49C1bfDPmvYWJHD0U5Imj1ciNygbdhrpvBYDiRKQTkNdp9xxvLssqAx4Em1XSj4zjfHsMhXkQSSYWQurm3HWgny7JCwFJ9jOM4IyWfOiKMuDUYDIbjhnXD1vczfsmiDof9iMCeqtanQn6JGJMj0LGkCsCt1ZsjJDV7VVLFk2gjSp4sIQoE6KaSTpUESlt3Q7VZErW9LCtT8bOzEa1V6xt6gxr+FbWtB3gCbpr7DQBuuuAbWDc4cmd8JIARaQciilzMeYioBfEG2Iy4JBuXboPBcGIzkcStZVkx4NeAcuvia47j/ONYjuE4TtKyrCeAS4GVlmXVHSQx1vvxfgiMhTv0ATlKxnCDwWAwGAwGg8FgOL4pTLHI21NG9SpMscZtHspS+gBwrmr6luM4Nx1BP6ssy3LU6+aD7Ha7eg8A37Ysq8gxW7lFa1HdiwRPjQvGcmswGAyGo4S26jVDZgk6pLa3LwFArGyAnKoikCZKFxWAxOLGGMAmT4w0WULufm3UYpPnWc5lAX/ktfpmd7Tash6yYYgsQFyUC+4mrAok6dR+4Gde+9MfOZOXL5jDJz75X3DXHjzLt7ZMnugMIImjqhDrrW47nAwqBoPBcPxSCAQoBEYnWgsBh3H0EvoZcJFafhL4gWVZ8w+xf85xnJePZCDHcZ60LOte4MPAFcD/WJb1TSQTxgLgb/HqGdzoOM4bRzLOaDB3IoPBYDAcJfQNXJWIaQXWQEa5t75yTYy6GS1urK12Pd6dbaQ23EYDzcxhBwEKxFXSohA50kTppIonWElB3dZqaSNZ3kaILPGSFNPtlJejsQR4Hk/w9qv2u2FZ/0Zqr21j0ffXsnHXMlgzU21USbuoUBP/TfG5nFB0IGFV+sfbAOKa/MIxm5HBYDAY3sT7fcvnA38cYf+9uGUNjoiPIzfLy4B3qZefIeAWx3G++xbGGBEjbg0Gg8FwlOmRfImlyF1I55naHiE/QzyZQuRIqvq3fe0VFCptsiVh4iSJkySE5KHIESJAgSxhCtis42wALuZx2qilgi4RvLUwvV+V1JuPWG1fx6ubC1Ja/lE4taWdDZ95Jzf87ha+8RXlwVXzdimg8DzQ2oSXl2Mr8BQnjlV3KjADIjGoUU3NFYiwNTHKBoPBULBtCvYoLbf2uFpujyqO4wwA77Ys66PAKuB0IIHcIJ8G7nAc57nxnoflOM54j2FQWJZVh1vT6Qt4MdUGg8FwIrEIOBfmVxQ/Yi0FrgcWQrByP4W8F7IzlLcJRnIMpqKcfsoGt72eFhL0YpMnQIFZSFmfWtqIkeYk2ghQKCorVFtoY+odg5JB+XVE6IKUCtqGzOlOmUd9lXhotf5qNqwB2pGy813qmPVAfg/w35wYyZSqofQ6uAS5DgBrgdR3ECv2iXANDAbDsWE/8K96pf4gSYuOCf7f+C+1xKitG11ao7bWIU6vT+vVCXVOkxVjuTUYDAbDUUI/0AsCzVKftyYoohZEZD4omwZrpnrtC+Uh7GAqAsCOvtNIlIlYLWAzhx1U0E2cJAVstz1NjBbqCZOjlwQnKRPtDnsOZ31ki4jZLUjZIBCP42qgG/gc8DH40Zf/DIAfv/dj/Oj562ATIoor1TF5kLquQY5/YXcpsEQstnV44ja1AbE8HO/nbzAYDCOTxyY/ypy9ecYvodSJihG3BoPBYDAYDAaDwTAGFLDdB60j71sYeSfDYWHErcFgMBiOEjqu6I9AIxCF9gaojEnzXMQSuhZJaaEtt3lLdg8AGci0l9NeNw2AabN6aaGeNDHmsMMdqY1aEvQSJkSYbpLEeUPFycYYYGdVL7NpFattrTqo3z0c5xWw7obzKyQ86Py5z/HwrVfQs2uGxN1qy+1iYH0D8Cdq4ns5PpNMfQgCTfA+5LNoRmKQAXj4WE3KYDAYJhxDhyFuh8Z5LiciRtwaDAaD4Sjhd1vdjAjBs6FrhjT1zpTkUtrlda7adT3islyDxLq2A5vElWtb6SK4JMO8GVuxKVCqsijnCBMjjU2eArb6qSFPyAvYdFOBXZXn1DPa4RU1TgkidjNghZFEUz9R22bCHcs/w0dXPSiuzHnVXgqUToVUHFiBnIAOmdr8Fq/XRGIjvK9JHjqkkFPseuzYTslgMBgmIIdnuTWMNeMqbi3LqgLOVq+z1KtCbf6R4zirDrO/S4BPqf6mA/uQFI3fcxznN4c61tdHAPgE8ph9HvLT5DXkGfS/OY6z9XDmZDAYDIYjZT/yLzwqq1suAXqg/UzJr6hzQC1EKu/kkZjXXXiZemuA7RG2fXgR2wKLWN1wAQDvLnsUgBhp4qSIkSaGJO2wKRAixwAxnCqvoA15oA+vfGs3XjxuFn7O1Sx691o23r3MizddjNxFNlnQWgGJCggskW1dSXWOPW/5Sh2aDwHPIKUF1+LdZtcgybs2vvUhAtfIZ9KFiPv1aWDdW+/XYDAYjjMKTDkMcWsS+4414225HZPaCJZlWUjuyk8N2zQDuBK40rKs7wF/7hwi/bNlWRXAr4ElwzbNUq9VlmX9heM4/zkW8zYYDAbDSOzHc+P9EZJ06mHovRaa66R5OxBB7lhd62T/diXY2htg00q4swJuhEyp1My9/6qrqJvRwmU8SogcYbKciWRZtlW6jyhpXi2fzjSVnGoqgyJUtyHCNg/s8Wb64Lc/yn9+5iN84pJlYk1GzatdvWvXaS18+RAi3jcydq7KDcAqzy36TjXmriaxbn/4/dCu5foKuBlY3YiIX21u3g+UI891R0l+ECqD4o7cDr6MpQaDwWDwUVB3mdHta8TtWHM03ZJbkJ8MFx3BsbfiCds/AP8E7EYE6V8DZ6jt+4CbDtSBZVk28Es8YftL4PvII/Ul6rgq4HuWZb3mOM7jRzBPg8FgMLwltAi8y7Pc0giZJAd+Xqpq96SCcNMiSChBfE+E1nsb+V7gL3jvKfepckHiALaQTcQYoLLQTSgzSC4SlGNsxDW5BJitphJRw6SATmhiK/M/8SJbUmdJeysiNFOIZbMST0NmYtC1ArFMr+HIsgkrl20aIHCh3A2X4dYG/sQH7mA1K/k6N5IjTIgsqVlSH/g+ruK3298L1zbBXQ3w5yq2+UGgvQOxvA6oa6is5+4cpyK3xAtlVV+jOqB1P+Izvtc3z+MxzthgMBgOnwIBqa8+qn0NY814i9uvAi8CLzqO02FZVgNFz8FHxrKsRkTAgjwrP08VCQZ40bKsh4CnEOewL1mW9UPHcXYfoKuPAeep5f9wHOczvm0vWJb1GLABuaP/u2VZTY7j5Id3YjAYDIajza4RtuvY1l3Qe4ksrs9DYzWsqOZX13+EyMoecmVhQFyVw2SppoNwFuy8SnRVRvH7XHAfvm8HfglLu1/in+/4v9z4udsAeOmJpTK9pYhF018m6HLES3hXHHg7h+/GOw+4WhZXwkX/8ytWsIYmtvLeqt9KeyeS4ArEG3k2bmKsT3IPr7TU8O/8Jenvx/je3r8AIHJbkqvKHuKen34SbkOs1c+vVZ1E1fVcJS7f2v17LiL0u4DKqdDld4BapI5pRkoijYnTlsFgMExKhg7DLXnIpJQac0ZXhOkIcRzn7x3HecRxnLdyp/sCngj/rE/Y6jHSwGfVagD4/EH6+b/q/Q3fsr+fXcDX1eps4L1vYc4Gg8FgMBgMBoPhBKPgpjAc3cswtliHCFEd+8GKLbcjJpRSsbYtiF/Wdsdx5h1i3+3AHMRJ7GR/7K1lWbOBl9XqnY7jXHeQPmqA19XqfzmO8ycjnNJhYVlWHXI+iGafOpbdGwwGg8GlHLGWLoeABbfDlA+LSXNO9Q6+zD9wGb9mWk8Gy1cCiD4k3rYHSSal/mOzHsmenAe+ATuvFPfnf+OzbOIM1r50oVhB5+O5JUcQq+iDSCKsrvvVhs1ANZ6Fs9w3gevgwSDXvfdfeID38XZllf4032UWuzh9+06Jrd2ids9TVMKIciCrlk8GamHn3DraqKVB3X6bmckAUVqo51N/8xNYhViYQVzBu1S/NUiIL2q5FbFO1wGP4Fl1A2pbM7BmEM9C/QLGXdlgMIwd+/HF+9c7jtN6iJ2PKv7f+I+1NFJdFxzVcR2tg1xa73onTahzmqxM9FJAM/ECjp4aYd+nEHFbh9yO/e7P7xy23wFxHKfdsqyXgdOQqCaDwWAwTEp6kDjXNZBvgM+fzdA3pc7ttjUL+PtTvsJWmvjT8h8zt1/FjmaQeNsM8hOlB0+o2r5tP4TZGfn9cfFHHidOivTpUbp/VsneDXM59cz/BeCVDW+TWNzLB+GeIEQ+IH3d/gFoTwMxoBXm17lJqILr9/OuikdZzHpWspoGmgFxpZ7btlf0sH9eWTWniGrrxBO7/UAKZj/fyuyyVree7yn5feJ6HYZPfvweBqvg55+QuVXTwTqW0EuC9SymkyoAWvrraTi3ma0dTQxlQjA/SLBShOtg11RK6/aRWjsdaoLQq26fXctg/b8g9Y3nIZPXQn4eUqRgvDNJGwzjgY5J19/fAeQPUNfyjuL9kQ5iOLHIH0ZCqbxxSx5zJrq49Vtqt4+wr3/7PIrF7eH2cxpQb1lWieM4/SPsbzAYDIYJTTPQA80Py2pDnle4lq+v+SrfXfZpfl3/bgCWdr4kgrCAJ2b7VBcBIKze+5Hkw8DlC54kMV8yX63jbBafuZ6oSsr0zjN/zybOoI1a9i092UtO9QhADB6ERbc0s5If8FF+irQOECJLdf8+InvwavDq6j4FRB+G1Xq/mqcuY5THixnuB3Yi44bxDMU9apvaL2jDNSir8lK4sGQt26tO4db+v2Nridw+0yVRfs7VzKrexQAx4iTdH2/NFTNJEie9MMbQbSHYpCwWKWD1X4nVO09xPPJ6INEkVuIG9REB8Jhc5JoLof37SDxvp9pmSg8ZjhU64VoeCUAfQARutWqvRqyKzWqfON73No6XN6AciUsH+UMMcGSJ5gwTmSHsUSeUMjG3Y89EF7f1vuWRzPQtvuX6YduOpB8LsQLvGGF/F+WScChqRthuMBgMhnFhuHvsY7BiFT2/mcEfLl4IQENVM9U9fVh7EBdkf53bMF5ayxJQBk165kcoEGAme0jwBjnCdKiNtbzOeTzNZhYQvWDAtYLGSbKDOdxw5u3ESXLWk1s8I8/JamxdTkhP2z82eHfvMJ7wnar214K8X233W3l1ez9i8Q2oV7fatl3GmGvvhcWwKLBN2mdCU+1WHuXdDBDlGc4hpxT2AjaT4A06q6tF5J4iWZnnsINn/+QcmvsbOLtkHWs63sXQLjmBKY39DN1QIlky8sAdavwbLpXlPND7SXGXzqht8y8VN+9dQPNjeGr9XDx1PIAnRF5Q7zMQITGAsaIZDo8gIkjjaj2PCNly5PvU4GvXoRDrkLpgWtA2ID8nz1XHNKt2LWyTiJdJEPP9PD44nFhaE3M79kx0cRv3LacOupfgt7CWjlM/I9Ey8i4Gg8FgOPY0A/8CN/4Vf5G4G4AHl/yKX5a8nxJ7SFx4+/GML91qXVtwlcgsfzRD42W7sMnzMnOoooNqdVAzDVTQTdWw7MFLeIHpD6REtBUQMarvxntU/3oMnZohq/bJF49Pie/YMuSuNdM354A6Vlt6wfOm1II2iyeQ25B59SNBPNoKvA3KT83wzvN/T44w9bSwFXHzbqCZHcyhltdpYA8vMweAKGkaaKa6pIMW6jmn+llaquVZ80yaif4kzROs5ByeJbokDUCAAm3fr6VLTShOkoe5ApAyTN1UsIeZJPuWkFmvXJxvBRbOllJMN+MFFa1VZYxq1Dn17sETI68BD/Nmt2gjLgwgAnYAEanVeN+LcjyX46m4f6CJoPzt5YHKC+XvrlL9IQaA0tmqPnQQEcBq3wiigVsXAQ/hldcy38PJjBG3x5aJLm4jvuXcCPtmfcvRYdvGqh+DwWAwGAwGg8FgOCCHVwpoXAvXnJBMdHGb8S2HRtjX/1x6eADD8H4yHJxD9TMSw92hh1OD1P01GAwGwzFnP2y6Hx6UZEq9SxL8a/gLfHT5Tzl1S7tYUbUVdCc4ecgXYLATYm2q/VyY0dZD27W11NNCnCSzlDtiLW3EC0kA4n2DWNrFuQ2xjLYhluESvDxLJb5XBO9ulcWruTvcWguutdeJgKXckAfLIdgvbcmyINGUWIMCBbC2IdbpvOqrbVjf+WFjBoBX4JRXJRnV7PmtXBKQ/IwvzpzPAjbTrayt2nJdQZfrutzEVgA3TjdMjg6quJjHCVAgq/ZLE6WXBDEGqKWNJHH+kn8HoKGvlYHSIKvtlbSU1bPuAqm1e/EFj9NNBW+QYPHfb2AHp8nnyTRaqGcTC+migvZvnep5iq6pg0uug9v3IwHU+oLuQqxmSYz17ERD2zPKkVjvKJKyJY8XH7AfiEGkWv4256tmXRN6PV5cuQ5US6jXQsR3UIcJdKn1UqB3KqQuxfuC9gB/xMTjTk4OL6GUsdyONRNd3CZ9yyO5CJf4loe7Hg/v51Di9lD9HJKR0ndLZSODwWAwTBx2wW13A/DCbTN44abbmXPLDnbP7+XCzrWuW/JgFgYyMJCFgI3bHtsJ7IGz1m+By4BfIHGzAPMzcAZekictbltAVfgRtJAFT6zm8dyD/eiEViqPzeBJIlbfKJfYX5u8m8jEJk+uLEyaKAPEKC1TQpsk8fkZEbndamw9rj43naBKU8ATvP3ANm/OZ3VvwTkVGuxWgv2QUX11lEx3D08RJ0qasHKeShNz3bXDPoeqEFkKBMgRIpHtJdY/hKXnkYdg5yDvzz6mElR9T9or5Lpsrz+FEFlipN3zPIk27uJaTqaFPZ+bSVz9HLip41aGUjGITIXKS6WME0D7ErXwEBIjuRpxYTYc/3xW3iIxWIwXk14ZhIzy26+rkDhwXeZL/91WIkJ3LvLLMUHxL82I6iuC98tSt+lEa4EKuEvHBwwi3z+QJGvafX4/RvBOfAqHkVCq4D7tMIwVE13c+sXiSMma/FbT4bGvw/vpGkU/DiMnnzIYDAbDpMaf4CUOtz7EhzY9zN88/P/43fnv4h9KbgEgGIDgz2CgDfYXoEOLznUwuwyCYeAeiuvOPiXbqaI4IZWO3+1DRGUHnoW0GxFrUykWnSfLPoNV0FlWTpuu6wNkCREnSYwBCtjYaiC9nCNEkrhrHe2mkrgSuoVymyhpqvv3ARDOglWC/JbW5YU0Ooa3H/mNrc+nH6xOCJ4s2yNK+J9SIlbe/rIpnNK3j/6yKWTD4oSVyPaSDsdI9KXIRTy3vFBmiEAB8jYEsxSXPdIZl7XQ1o+iVezw3Ff3Qgmcmm2XBlvmc0v/13HmQVd5Kc9yDgB3Vv85VdWdbLplIfdxFenPxdQheXZ+5HT4zTUiUJobfBcgiAhdY9E9vpgKNMJc+Q64orUB+Q74A9vywIdV+3BNoj0tKvHiaTU6cZvuH7Wv9pxIqPeb1Lb2IDwSlPjx1qu9Oh+9dyPfP/PAZSIzdBgxt0PGcjvmTHRxu9W3PHeEff3bt43Qz6ZR9NNiygAZDAbDicRmIAiPbObrs7/K6TufJ6YSHd2U/wYshOqfAK/CBvVjMw/s7IOBJ+XJafV8xDURRKTOxivR47eE6iogOkmVvhvrTMwViBuj+gH9fO3pREnTyzQK2PSSAMQCWkkX3coXMkyWUmWdDJNzf2Dlscmq3Io2edLKBTPGAGlipEpkW2mJiOTpgZRXQxc19wq8DMxZin/c24hr8/BfFX1QwhBUQMmeIUp0h7VQ0pGCUghOHfKEcljGCPbjWYr1ODqZV0RdJxVE5ESUBVqXOPInylLHWmGY3p/iipLfAvCH8nnESbKAP/JZ/o0NLAZgNSuxf3Y/D/MeCths27sC3qdKt2wCuB9xBmvGcDwwFTgFSq/wco3VUGxp1S+N3gbFD14yvuUuUH+i3t933vfOsONL8QQuanm+6rMBr9bGg6uQeIlnkAdzRuRORExCqWPLRBe3uiBDLbB8hH3PU++v8ea7zlrf8nLg3gN1YFlWDahgHbeKocFgMBhOHJRVbtfNvGRdx0t33A7A1s808elzv8vy2S9ABs78heyWvhua+0Xu/BEIbvEKq+9vgdla8FX5hlACjqXqvQo4XzZlLoBID/AqPFV1Nkll5mmhnl6mESNNG7Wu622WMJV0kSVMAZsYaU5SAbTaBTdBrxpW8iWGyNFJFQl63R9Wfmtvmij9ZWlKuoeKf9RrQa5f+vFvBhGSOiuzTsvYjfeDfhPFluBNFFundbYLbfGyEcHrE7G6Xm+mBNIlEcJZcWfOhkPkysPUtPTJmP6SR6iw5CxkAAAgAElEQVT1PTIPS/2ObOrfRkQJaCcCF/bJz4QLS9aytmoRcZI8zsWkT9nN3gdnyUE3BuHeZYjLsmHyogPcpyJxtcvgcrzvuhalet0vXAMUB6z5xa1fuGZA/dkVB8NlfP1mfO2leO7Mel2/V+KZXZYBz8+ErpmwphX4kW8yxqNgolBgyqhjaQsmodSYM6GvqOM4DvArtTrXsqylB9pPtes//V+p4/z9vIxnzf2QZVmxgwy5yrf8wBFN2mAwGAwGg8FgMJyQFAgc1sswtljDdOD4DmZZDcjzU4AfOY6zahTHnAb8L/JMbD1wnuM4A77tUeD3SPh/HmhyHGfnAfr5OPADtfptx3GuH7Z9FrAReZS3G5jrOM6YRnlbllWHGw/8BbwChgaDwWCYeDQCOsHQANz6dhb97VrO42kuYDUAK/ufJHI98CrsfFJch7RXbAWwwJYMy/uBGSpGNFaL+CNdidy5amHfTDHV7GYWNgWSxNlKE0nlStxNBVlChMmRJua2x0iTJE6OECep7MIxlXAmTpI4SarpoIFm1zprU6BFpZeopc217Mqcuwgop7pYIU0oI9agXCRI2o5R82SfZ1XVFixttdV1e7XBooD4XrUhxrIePKNZGV5W6DK8Ort5X3uAIqvuYBgGSoNk7TBJSt0fhTlCRJUl+9S2dtC/APqQC59R/Z3qjT+oLOmBAlgZvGzRBRlv/4IgSTvOHhp4kCsB2MEcHvnBB+FOxO20WTuF6Z8KazBMdKLADLX8fomzbUBe2qKqrabaGquX/e8B3pyaNOPb7ndd1rG42rLrtwj34sXpDndL9ocr+MdvRmrmbgK2OMB/q43DI/KOR/YD/6pX6kdK5Ho08f/G/07LSirqRldNtLt1gOvqV+vVCXVOk5VxfVxgWdYy5NeBptK33GhZ1ir//o7j3D28D8dxXrYs63bgRuRnwDOWZf0jIkBnAV9C8lEC/POBhK3iR8DHkfRzn1EuyN8H3gDOBv4OUZtDwGfHWtgaDAaDYbKxC680RxRugo03LWLjncso/bS4/HaWVPPxxT+DPMyuh6ktnnNgB9CjYknLbYjpBEjnwv67guyyG2mmgRhp/qBuY5V0kSROC/WEyLmxsTlfMqgcIdKIA9KznINNnpa+ejKbypm/3Ks2FyJLgAJRBtjNLFbxQ7ePrTRxNfeynjMZ8GUunkkzHVRLMiq7wF0l1wJwFfdhU2Dl+av5JN/n//FV1+156Z6XRByWIWHL+pdFJyImX8FL86hdjKsQwanzYvnLHIXlfbDKE7OyS54BJeyzhFxxmyZKF5XESfK/tTFOK3kFgODraux+37gq7jmoE3WBCNphv4ambh5kaqCHGWU9NNVL2o67uJbaT7TxwCeupJFdPLf3nbLzHUERHfesgK778b4zYDLbTiSCwNshcamsNiJxrTqhk2a4kIXieNrh2w60j9+VWYvXwLB2vZ8Wyr2+eXRRnI3ZT4ParwFosCB1tbSvuZ/iNOyGY4WJuT22jKvl1rKsu4E/G+3+juMcsFaOZVlTECH68UMc/gPgU47jDB1iPpXAo8BZB9klB1zvOM73Rzfjw8NYbg0Gg2Gyo2rwcLa83X4m87/4Ik9yPtO3pOBzeP5JOlmU/iF8kXq/An70wQ+xiYXsoYE5vOxaYts4yRWkOUJFCaFeV0pQi1WA1g2zJYtEF5LRqhUvMc5SoAamn/4q+/aeRCQhYjSztlx+bLfCvA9spKNQTc96sWZdtORXLGQTP+ZjtL9WC2vUr+vtwFwIXrKfweapUJPh0hmPAvCX/BvnFJ5lq91EiBxN/WJBKgSmUPLMEPwSSax1Mp5ZW1tSy6Rt/1JJ2jS1e5DXqsrpJeHGEWuLc4w0WZX5uYV6t56uf584SdcS3djTKmWEdNmi2mHjayuaTlwFnsjW1jZ/QqsKaJ9fxg7msJtZbKUJkKRem1nA2l9dKMfcAzxyv3eQsehOEBbB0is8M4uuUeu30Or14SJ2uJjVInV4uR+VqJt2PKHqTzDlF6w643IEEba6vBDIHCPD2qD4O6stv9rOdzdI2aB1bz7144bJYbn9VsulVNQdLAKymO7WNJ+rf0yvTqhzmqxMCnHr6+8y4FOIOK1E/l28CHzXcZzHDnWsr48A8Engo0jejxLkmfMTwLccx/nf0c73cDHi1mAwGI4zrr0Zrofvnf4xFrCZk2jjlPVSVodnEEG3E7gG9leLgLvLvpYuKtnKPHKECZHlZeYAIs6yhP8/e28fH1d93/m+j8+ZR83Ig0e2hCyBHOTYPDhxMAEayA0ppoENyU1Tdsm22VfoDd2kT/e2Xbbp3pvd0qfXq9umbTbbbpMte0P7SntJS5M0YRuyIQ00TgLEUBdcMMHEohJCskdi7BlGM6NzdO4fv9/3/H4zlrD8IFsyv/frNa85z48j+3zO5/uQhBlLVeTD9DPxyFYVkvgo5oF3H+p/wn0AMzBSNg/qdluSEUwV5xv093a9jF0g5wFUleYvNSDIQyjNBl7SG9mqRm9V5wTAFbB+dIqPZD7DY1yThDz/KF9khDGqlNjNQxxklKv0QXyNd3EZzzBNP89wWSLiS1QZ5WASel2jyHO6zuM2vk+eBuMMU6SWFGzJ0GaQSXI0iAiSYluXcJCIgBKvMM5FjHAoCdneeFhXgz6KCRkFJbZF6DatYfR1zKh7+uKmjcnLhkOMsI+3EOHz2egnyfsNduqmDA98Q4cx7wXGDmEKAKVwBYDOJjngBrjtGvPyxxaJ3S6tHX7cXd04RP3N1DG/mynM35JM746NFKFqO7d9eposa68zYK0j1ZKlIFVgfcvf7y/EgAfcvfglOC9YG+L298dvZcMyxe3sRINfGn5ARlfVOa1VzmrO7esdJ24dDofjfGMzjP4UfBxu/dBf8Z3obQz76p/5/8Sv81PRn9DvT/Mz/LdE9ElebYTPOMNU6OMHL+pC/fenlKO0D/jAPBzQbWgOYHLtxjDd2g8+j7Im5wF5mJLSzIeBHZjQWPnuV58C6kG/AkyJ3XwAZQXJE/0x/Z2yzvlqlFjQLvadQ0owH0A9rO/Ui2Vh4Jof0I4yjPiHePKfrk8e7N+861FGOMTfPPGvIQsbtquWJm/zv8NjXMNlPJM4sr4+ljYZSlT5VuvtHJ3o5+JLngOUo3s1j/Ew7yStXw2AelFwO59nG8/xDJexg6cTt/VtfIcZyry99S3+IbOTQZ10O3x0irlCitD3KR5tqpZEcgmamBDnMokQOTJcoE2ah9hNgxwvMJq8lBhnmOfYxou/t10ZuJKmW41hyIMJmfCsvl/oe5nDhTSfSf4V3HyZ+tuyc17tMOG69R3SKTplXhMVdT6gp8nfoTivtjC26e55K9NkWF5C2ctLeyD7OGz3V6bLtseAh9CBAp9CJbmfb6wNcfu74+87KXH774e/JKNn/Jw8z9uE+kf7apQ5+FZMlYNl1T9a5n7uBn51mYu/M47jh8/Efhc9Fiduzx5O3DocDsf5yC0wcg2MPY/KtL1eTf4oygktATeTuC8bfuElSn6ValTC9yOO/PlF8EH9PDM6pB6UKzGUPHT7VeXWDqHEbbWByev8NuqBT4SQ7QimgIvpzAEFJZx2A1eiRNUjGAW3HEGVQj0nSYj2rBq/No9Vm8q4TRXUg/sQpgrHdpQ4mEIJbDnEAHXOgV6mb55Cn9pofWyjERN2OKgtECqYsNPSPBsvfpkjL17I1oufYZCXk1XexdfYx042Mc0w48mLhxJVruIJ+pnmQiYZPHqE2nqlHjYcbprCWZYzd2wwRcUvM8kg4wwTETCnc6XlJcZ93M4zXMZlKCf8795yq7om+2N9/eYw4ctzqHt0GHNfHKfOlcB74X0Y19ZGwounrHE7HLiOenFjLyvTBdvp7XZs7T63tqC1v2Ve1ppWso6juzWQTO/e3gTq34ovzaBClOH4v/+1zNoQt/95/P1cMNRzgjUUr0y8yseGvyCjKyFuX0vonZfi1tWfdjgcDofD4XA4HI4zwCouKDWOeqP5Iyda8DTZcYL5h04w/7Rwzu1ZxDm3DofDcb6SwoSS2rmUVwKzkL3Dchsn4OeG4A+PoSocPWitsxkTTngLBENqMATlCn9db1NaR5xq6OEG4E2oEOZnObkqqxKiLO/HN6MssRywyzi2kiu4H1MJVpvabMfkH9Yxjm/TWjdAhTjLvBGMU9X9ar67Cq29DalGO4RVzKfJGza/QJs0PhFpnVz7v/EtQnwGeZlh/pkMbUYYA1SbpNHWC/TMLHScfrNHtUqa9AepUGaOPMP6v/oqJRrk8Yn4Eu9LCof9Azv5/qvbqP/hRuW07aXLlX4G9Vt6HHQla8fJoku+DG1R9+pWOnNoJXe2yuIurPw2D2LlsR/TI4uEnEqIsO3idm8zoLNAVHc1ZLsIlfx+7RZB0jLIDlfuDnOuAL8NTDX0jHs4f0KU14Zz+xvjHzgp5/Y/Dt8noyvh3P4aqj7R9+I4nj6VtqzL3M/daOf2RDWUVhrn3DocDofDcdrMs3iBoCfVV/Nua1oK/nCpYkL2Q+hjEIronNafUC9zug+rthh/6STXne/6PogKoR0FajB1g5osOYgiOqsoIQdKqIr4ncA88DetT5XOqrN1TB6i/cBv9w7Fmi6iReY3McK7nuUH+y+HASiMHiEMlXvy9+vfzhbGiAj4Pm9kJ/uY1uHXIxyiP3OYdM9RUi0lakEJ29D3ydFgsKMClQpzLlKjQI07uYeW7kn0HG9kvGeY3/i5/0R9dKOKEv+EXqkAhJfp8+/H3J9l1c18nbMBVfTsFhPSP8riPWPltyHFoOQ3ZOewVgBmIJQXOhK23yVuJWdWXqR0590G1vxu8SvHZocY28dZxYTa22H/sm6ha/k+4C5gnz7Gz50vwnbtELEuKXi3nGVXkjiOlxsqfN7gxK3D4XA4HGeV5VbJtd1UO5f2TDysTqOEd47OYlGnyjG9zZdIBEBzM0zsgMBTD+C2O7YfJTR3YnIIweTnims2hXmgl2JA2/U6Ipr7MH1E7aeaLJ2Obh20CWuEcgXq+zcmuZjP923g+eBNrCs0GOyf5JvRO+n3lXN6NY/xDXZzw/qHmeRCbufzAOSZo0GOgIiAKHFnwbQpapPBJ0p6A29hjICIP+35EH/2Y/+G73AdR6oXqZUO6OPbD4wMwZh279kE/AWuyvJSbABSEGhhKy8yJHe1GykIVV9k/hTW79X+G+kF5o93YOWli9DEOPEy3/7928vJi5rFjlEEsaybxVRjlnXtvwPZRgH1dwKw827Y93lUhIbjbBDpfw2Wu6zjzOKuqMPhcDgcq56VEDSzqIJTRU6uoNRSiMMox5pT08Ic1MvakdSzxJ2t0OlYiZNrhxYLdmXaAWud7Xr5Ekbkyj6kl6gUgRKREOr9LPoU5LFQ6GGSQdLZFtMF5dw+7l/DCGM8zQ7apPkK7wWUozvIy/iEBESE+MkDa5s0IT4Z2uRpJOHPFzLJMONUKXEXn2Af3+Dnb7sHgFTfMeY/2avO8yDGtatsQaWyzWJUusPQC+xW4ccDSywSYuorVVm8j6208xHHv5DvjB4opUxIs2AXfwLz25NhefHSh3lZY7cgCjjeXe7+2NuSlzP1rv00OT5ceQBUAbhZXIj72WEV59y+LnDi1uFwOByO1yXzqCf9Xs5s2xlxlh9DieYrgQbUrVBO6dVbRbmm8jQijpM4u7a4DVGabsQaB+WyiaNlhx7bTpgtrGWeuF926HTFTF+gh2bQQ7OkZrZLaV7pKTHNJkpUkxDjGkXG2IJPyGU8S4lXOh5YlYujHF07VLFIjRwN0rS5hsd4Yttl+qpdzc/ccK8Rt3IefUBlM66C8mJcCuRg51BnxWGsYRGz3eKyW1h2CFtM6LK9LVm30rWtOqbatxju8tscoVP02tWZ6Zpnh/TbQrr778Fe367KLNsA9fd0YAuM7Qb+HMfKs8C6ZYvWhRUOSz4XeJ4nxSGKqL+4Z1DFJT4Tx/ErK71/J24dDofD4XA4HA6H49wy4HmvXYtpNRXReg12W8MbgXfoz8c8z7sjjuO/WcmdO3HrcDgcDsfrmpVyAudQeX5PAjeQuGsAzEB9i6oSLK4WKHdyBOVQSdiwnW84iun7Ka5ZBZOrKxWYZXk7l9HujSuOXZXO/qLdlXQLQFXNrOuiU2PZLQz6kxzWhaZKVOlnmkt5hgxtysxQ1gcXEOETJqHKPhFgQhEDInI0KFGloa/NW9jHX9z4Pu658U7+7tpbTRGuh4D6Lmj2Ay/icm+hI1985L3KLe0uHCUOvfyWbLc1a326Q4SzaHd1BhMGfinUn0T1eJ4niVKo5FA7n4eDQ52hxBJCLz1qBdsplrzfbrdZjrM7XJlFxu1CWXbUwgDaER7Vn/Op7+3qJMRfdkGpruW+t4xVzmkl4hPwNPAlVJn3SdQf6DbgJ1Dth0rAX3ue9544jlesQp4Ttw6Hw+FwOFYICXd+GCUQRJANAU9DfRT2Xgol/bwmVWuHUEJgAiMIRKAUUAJ4yJq+D1MdWcJOB/QyISYXt1vEDtApBmwxbR8PQDNFQ4dWjwfDpDPtZLEInxpF6hR5I88l4jVNixLVJAe3alUdqlIip69PP9Pk9XCeOVpkuIonaP9Ehj3Vm9QKo8DHQeU253DiFlQbKoD3qPBbO5TYFnjyssLO55bpEsprh6YnolYqlEsxpjHU83pNb0xe1uTMvMpjwJWwd4uaJVWbR+gUqlJh2T5mO2cXji80tdhLmO7KzHa4tWxrOzDlwdT7gd/BsbJEVt79cpY9T/hkHMd3LzL9MeDPPM/7CPBpwAfu8TxvNI7jM5kPk+DErcPhcDgcjrPAmDX8EkokzAIvwg23qMnSHkhcM3G1oNNds93WJqrqchbTPgiM82u3eLF7grLIsAhoEUO2SCrBQthDPfTJFhpE2smd7umnQZ40bSYZBEi+tzBGkTpt0oB5kG2RIcKnQZ4iNaqUElc3TYt+pnkPX2aEQzzzUZWLO/uJzUog7b8GeB6Xe5sC9O8myHcWdFoMEXxLtY0SpqTy97P6ew4jYu1K5SJy0ctIe6154ACE+sf66AYoldXPXwQ4+lsqHNtFz2Se/O67Wwt1C9ruKsvdT/ZSaG0UyOZh7Drg2zhWjoWTKCi10LncW+nM8F4zxHHc3aiqe/5nPM+7CrgTGATezwolgTtx63A4HA6H4xwgvYGvM08jtzVZF0QsTPUowSE9baEzbNR+jCqhRIO4rPKwbz8ihnS6sN3bC7uWswWFOMdJz90szXoWCspea9TztEtpyMAkF+LriskAz3AZkwwyyCQ5Gok7KyHIAREVyrRIJ/PSpAmocgFVBnmZ3/d/CYCvfOw9/PXNH4SPAo9ew5npd7yWuZpEdI7S6Woudq/rXdPt0PQpoCmVhJ/C9JOWH4EtYnOYH6yIXvkt54Ay6sXDU3peP1T7Uf13MREH0s7KDp+3n8ql5Y9d7Crg+L+H7nDqxZC2WwW0uJU2Y6/3FyQrw2lUS55aIzm1p8pnUOIWVA6uE7cOh8PhcDjOJ2aBCZhS7uTQ5nGGGedQ/wj10aLqQbtfL1qg0721cwxtV84WD3WWFrGC7ZhV1OFQR+UDb6czH1jctABoKpW0UJqnVi3SCPIMlicZZzjpdTvCIWoUaZAnwifQ7iyoKspzenqbDFU9L0+DOkXKzDDCIdq6KnOET+vNGb72wLuY/82t8MmtwN0nvMLnHylUOPJN5l5Lr1ih282UlxX2fZdc3DFQkZPCHEbEHtMr2e2txJ2dh6SnsXyPYH6Mdnst+Z0Pdeba2uHw3SJWIhGg09GV8YK1nEyTc+x2p6Uf7hAwkIcpOV4nbleC6CSqJUfnYbXk1+AZa3jzkkudJk7cOhwOh8PhOEdoEfGAGqv+ZomdPfsAONQzQr2vD3ZrRTKVMrmItnPbpFMEFJYYtukO85T168D9wN5p4Cl48CblCgLchhK7B+kUU1MpFgopFgJ4sVqkNlJkm/8coITqJqaZZJB+phNHN89c4u6o4jO5RPi2SdMgT4sM/UyTRuX2vouvMcw4Udnnqze/X4nw+y/VByE5oa8XdG/mUq8alfZPdjEoQZx4++WGTD94DKVuJzDFqXLABpRbO0tnm6x5zI1PWRtM6XV6reXsNylzJCHOU1vNMVdZ+ncqzq4tVmU5iVAosPhv287flWsi+eV1MD2pHStBdBIFpc6jnNvlcFaKYTlx63A4HA6Hw+FwOBxnANXberkFpV5XUuwya3hypXbyurqiDofD4XA4VhsPwthPA1C/byPtD6fJ0WALY+QvmWOmVVaLDcDRiX5oep1PLxU6x+2WP3bBINu5E+f1IHAA0x1lH1C929rA43Dwl9Xgb6fUOnei3NwBa1tNfRwVj9lsme9WfhiA6Tf/EyMcYhvfxydM3NkS1cS3jQiSglMAPhFpWsm0PA29zisMMsm/4c+YeVeZxx99B9x/u17rr/WJwPlfRVmHJI/0mrDkxdxLu1qwuPOSe4t89+oFhzD5s+LAFoEvoEJ3bffWLh4lOzkGujWUmpbD/NgkhLmmpoU6pXIipdapAFeAVUjb/Fa7nVnbwZXfsD0/ZPHwa3Gzm8C1qLZSjhVj4STCkhdeX2HJH7GGH1mpnThx63A4HA6H4xwybR7G74OHbt3Njv6nKVNhG88xk1HitkYRhqBWLbLQ7DEitkBnmHJ3r9omqmCQLXjuQz3kH0Dn9D6hZ3yl69jmgd/Sw5dCeCk8+qbO/Ui7oore18FsIlR+EFxOdLnKtX2ONzLIy4DKxQ2ISNNOBK48DPtESX6u3Tpohj7StHgL+7iRb3Dw46PMfkmnre27CSXQxlAhp+dzLuUGGOpV6a3d+aZ0TbPnyf3vePIVUdpvbUCL1AJQfy8qnFgKMD2PClWWfFurzy7HUMI7h8nbtZGXDjI9p34zUhlc2lXJOSzWukqOS77t827SWVncXl7EfRX9DmSHnvk0jjPPaRSUWpV4nncH8Fk9+mvdLX88z9sBzMVxvGQTZd0K6MN6dAr44pk/UoUTtw6Hw+FwOM4xf6y+Hv1pFv6wh3/86E7esPkF+pmmgeotK98Jdp6sXQVXpocoJ3YPRng2v2zWv38HqiXKks9jXTyrlt33Bdj3IRjQfUxvRgmtOqZCs1RYPgAvjmwn/5EGDfJJReQxtlCkRpkKAG0ypGkBKh83xKelC0nZbYRKVLn48BF2bHqKHf7TPHKtFreFXthzJaZ6sjzezdFZEGmtYufDvle9PFgq31QErC360Ms3u6cHmOJROkLAzmfNpqCyGfOyIED9ZjZg8myxhlPWsdrC18baVjMF9V51TBWMeyvidrGiaAWMwLVdWjuvuLvysri2ITBh1/RxrAThSeTcLne5U8XzvOsxlQPA/OsEMKqFa0Icx/eewm52oXrXfhP4KuqtyQzqV7gd+CCgG3YTAR+J4/jVU9jPsnDi1uFwOBwOxzlGt2Gp/wl88qegkuUHI5fzg8Ll8D6tTsaypsKt3f9WxqXSsYiZzwH1BpBHVcOdBp7UM1PW8MkgIvErMHWNGrx3GrgSrtAxsuLighK9B+HZP70SdkLtClWldof/NKMcZIa+JFxZxK2v1UqkBa5UXp6mnzIzcBj+9eTfUNxZ45G73qX286AHe1LAdcCXMSG2Eh4rbuNaRc7n0sXbOdnjS7XI6S5G1lFYqWG20SxCMwRSSkSOelDRqYLZy6ByE4RPoEKX7WsqIc4S5jyjp9sthcC0FQIomxZTtvwQASuVju2CabZwtaMR7H653YWzpAJ4Eoq9lPB2nAlUn9vlSayFlXdu7wQ+tMS86/TH5t5T3I8P7NafpZgBPhzH8ZdfY5nTxolbh8PhcDgcq4SXoP7H8OmbgS3KFe3TamY/6gG+BDyIcbm+ZK3eBMKv6pFpTGKthIoKpyv0ZlEGhZADhtQxDmAE2B5U+HNJTZsNlNP6SHMzmV1/Q4kqg0xSo5hURW7TpkgtEbst7dzWKLL18IQ6pW/Arfwdb9ipXLgfjF0ONwCP9kLzJoxwl/PegApZ7g6VXSvI/brO5Js2Od7BLNEpcO15Ev6bhLDPYMRtDjish7UbXu1VyxZ6jfAsoX5OE7v0hFh/1/Qx2g6zVE5O0dlSSO5BiDG38kp42ucjQtbu3SwRCnJ+BY4X+2HXdAl9fhR4sIF60bPcaAWHY1n8LSrk+IeAt6Bi+8uo6sizwD+i/tW+N47jFc+ZcOLW4XA4HA6Hw+FwOM4AqynnNo7jO4A7TnMb9/Iajm4cx4eB/1d/zjlO3DocDofD4VhFzKGcx7+EB3Pw4IiefqX+HqKz3+gjGGdyqVDjlXYsvw37vw1shqk5QPrPjsJDeXi4H34OXbwK2AnP7drGCGO8kedokWGaTYDKsfX143GGFjl97HkaHCun6N07D5Pwg50D/CJ/AMDPV+9RjvEVwN5+TKThPMrBfgnlJkpOLnraWglVlrDkXuVC1ul0MMXplFD1gOPDk5tow1JCkJ9G/S4C6xuUy9qLum45qOdgn87H3d5rCj/VgYpu21nQebPNGNPKU/JxZzHuLXS6uymQXPImJhvS7mHbnVu8VO9bQUKwJQx7TB/rBCS9dtesg782WE3i9vWIE7cOh8PhcDhWEccwVVxDVCEnUMJ1FCUKVmuVVwlz/bb+fhwYhXAD7L0J9miBc2cvLx7YzosfvISrLt4LmH6XLTIdlZMzOjwZwA8j2AS8Cm/44hRf/NH3qRnXY4TzEDCmQ2IngGoZwq2YSspCoMfXgtDZYAbrqHPsFnwi8rqrJUvOaQVUVWw5XwlblyJQ3e1+QIl/q5LyhA5RHtGrSohzRR9X6Fn5vSJyy5iXCPMk4cqFIbhKTx5BhTz3WeNNTLsfQcQuLB5+Ld92Tnod2Avsa6B6ANm/AcdK4FoBnVucuHU4HA6Hw7FKkR6hwlrLFZxHifNe2CPiHLjnFiAPQYqvfexd3MA3k362ADOUKVKjQf1SCO4AACAASURBVJ6SVlB5GvRMLih9lgVm4Lf4OAD//kMZ9vyHm0zblyv0hgZQoncqpQWuOKCShyuKyBY8q9HN1a59ASVsu1vk2L1cu8miRGgdjFsLqpWPOLaHMQJaqh7nUDmxT5P87uoHYd8tSnwOYfK+B/Q+puisbpyQgkoO6IeCdoFv1rNGUfVkB6zFxbldqs9tdh7CQIlp25kWwVvFvOyYAvZ8XZ+XE7Zng9VULfn1iBO3DofD4XA4HCuKhKSK45wCboAHetl/1Vu55sbH8IkAyNAiTZsWaQIiDnIJAJfxDC9t2cDmrbOqLulRuPbwPwLwM5v+iD0jNylRY4suXciK/ehK0kNdx5TDFNzSFatXZdiqFoRSPdgOOwYjBoXuVkAhUJ3BiHpZqIYStnahp3lMv9qcXsaugTMDE2W1urxEkBDpIXT47yJkdUizFKcaRTm3co8CYEjHEjcz+tvrcm5jCEIIIus89QIVTwnrT+jtPSjFrmZR93gax9khOolqyS4s+czjxK3D4XA4HA7HWeVJ4CDs+SXYC0/fuIMdVqh1kRoZ2jTIU9VKdY68agu0YVbplPWA7hTZxwzZD8zSrG4wIbJgqktfhXIHD+jpITp0eR64GCPwQIXNvsTqEUNXokKDMaJ9MXFrt/yxhW5Vxh9HCVu7N62EDEu1YzCteqSNUhHzuDwL/CWEO4DrTV/jEZRwrVrfcqx9KNE7gOn6KetkgcI8BBHrgoiFUAudbMucF4BMF1ELqkduEMMBHf7cBB7WnwDQ+didwtxxNlg4iZzbs9AK6HWHE7cOh8PhcDgcZ51jwOdh6nYef+IdtHYpt+427idNC5+ICJ+2bgXUIkOFMrM7p9kw2IR9JE9xu9jLNesf55Fbb4b7MMJPCi+Jsyihr+LwTmhHlF46CyD1o3J0Z+kshHQuhNIGkiJQQb4zp3app1hb+BaAA7Hejh1yLQp5CHWuQhF1rv36M48J550jyQM/GMLBG8z+dmLaQNk9a/swfWxF5AIEMesKDXwtWOeb6a5z8I3otadNpIyYf9hTvwOAe4/pY70HwtXmvL++cAWlzi1O3DocDofD4XA4HA7HGcDl3J5bnLh1OBwOh8PhOCc8C598GO66NplykEu4iicoUsMnVKHIwBgj+IQMM05mfZueTQtwVK0TDQfcxv0cvnwTz+680hiRdYzDOYJxPccwxYqqZajGGFdzA8qhnNcrHbNWknkBytVdaYcwp49BF8IqYdr+2M6tfIddw6BDsb+NynfeYLaVhB0HqNZNtkPdb80f4vhCTCHq3J/Xo1tVru0oyqkNMe5tn/5IWHKgc2Gzpgp2FPoqz9aaBkAzo1zbMR16nEU5tSPAPZgCVgB8HRVe7lzbc83CSeTcurDkM48Ttw6Hw+FwOBznjAm4PsvkoUEA/vGJa3l519/wHr7CKAeTaskvM0hEwAuMMpPp48rBZxMBt4+dNMipZa9twphWsSPoFjgogZU1u0zayoRA1oPKFjUvbKAE3gaSXq+gxw9jHh37MdWrRQyfaVJ6H5vVaJXO/rbdbX+EENPntQIqx3lWL2jn3ErYMZhcW/QyHuq8GnQKYrvw1JgaPLDVvCwY0B87P3hEDxdUKLLgBxFR6LNQz+vKx1ZubTWlzuEBOsPMK6iiUWN/jRLeX8WxunBhyecWJ24dDofD4XA4zhmHYeyrHPF2qNHd8NDndnNb//2E+FygxW2bDFVK9FEhwufIpgKZSDl9ZWYY5GU2Mc36vipHP6CV1VhWCdkQJQT36l2WUEJRBG4dI8aaee0GSvGlw3rGMYxzG6LEnZ2n+5Je5kw4hylUQ98N+ls7l9LDtdudtbHbAwFM7NHHhz7GXmvhHKb1T96altLDvfoj689iKijPk9izAUpnDmGErBSmlvzYrKp0LEWjUtm2OYwghDBlKh9PpXQLH0xOLSgtXQUqn0Jd59Xa7/n1TXQSfW4j1+f2jOPErcPhcDgcDsc54xjwFPCYGn0IFgbu5r74dn6W/8agdgcbulpyhT4A0rQo+SJ8VTGiElVuyHyTJzZfBcBkMMhCX4/pfdqnd7kdJZRKmNY1tijMopxEgFBXKqZIp7s5hxGBIoSf1eMiiHOcfBEq2VYKFS5cNrPEje0uJhV2TQsxfV47jhnrGPtRzvSVKCUq7mxvZ9/cAKjs0vMuRbnAMrxVDd6GEbbikCfObayupW7hs86ueIxyb8m2WQgDqOprPoW6L1Oo+yT3qPkMKsR6FsfqJTqJnFvn3J55nLh1OBwOh8PhOKd0u50NHn/1Gt7V8zXKzADoQMeQOXI0yDPOMHPabaxSIsJnG9/nGS5jkEk1vVAizLZoNzMsBD1m83Zf2O62OkHXpyoz8taCErqcslbsBa5BiVkt+phGhRRLxeHu85ResimMYOtHqcRL9Twr3LlJZ9sfodvBrWMVQH6MxZnG9IAdxTi6IxBeqpxUuUbiwtbzEF4P9RkYKZu2Pn2onrcjeryAya1FDy8ibKPQN+1/Qs+cRwV13ffIeUtbpicxLw4cDsdiOHHrcDgcDofD4XA4HGeAiGDZBaWWu5xj+bgr6nA4HA6Hw7GqeJZ69XLaPRnmdLhsiSoNcszQxwxlysxwUOd8TnJhsmaJV0ij8jkLPTXmWnmCIKJez8MVOux1AuUuTmGcW7tIU92aLkWb6kC9F+Wk5uksIDWPClueozNnVcKSj2EqL3dXK57HuLWgQpLL5lgKKXNsUgBrsR63TayQakwhrddEzuFFffygHNxpYEids1Q6luOpo44vQIUjg3JtO0KZVX4toIpDhT6EPgvQ4d4u6Ok0U2q74pJXMPnRYzHwBT0yy8oU7nKcSRZOIud2weXcnnGcuHU4HA6Hw+FYbTyQ5VsfeTtv4zsA+IRkaFOmwmH6aZDHRwmliIAGucQFamgBmWeOelgknW2z8ZJxjtQvUttuokSbCMIQE4ILRmTVOT4Xt1rW02yhKtWSUyhxaAuwQC8nxZh6rembUcJ2zmwrmzdh03ZFZzluOd7FnmBlegkjypuLLHcc85iw6CdRocy7oTkEU/1m/zfr7TeBqzDtftD7y5pQ5HW6YNRCM60KRQVhh7CNRNhW9IuAKp3XfR8w9hQqx3Yax9rBVUs+tzhx63A4HA6Hw7Gq+AoM7OJ/vfgvuPpilTP6FvbhExERkKZ1XDhjmww1ilQpEWjRK4WmfD8iQ4v126cAaAzlmT/Ya1rLgBGD6Gl1OhFh2US32rGrDqeAWM84hnFu7WTYzXp8s7XOJgg8CPKm2JWcVhNzfN0Cu4TJv5XdiHMqw1fpeZK3umykANa3ga0Q7lbOKpiq0u9DF4+yRHzYeT+SXFpZl4CFJlC3xGzTOs8m8LBe9FFg7AlUPvDrpXiUVOIG4+inWItOtRO35xYnbh0Oh8PhcDhWFf3wceC2FGO/qvrPzpFnB09TokqGFlVKtMgAkKFFhE+bNDWKNHShqUaUp9XMEIY+xfU1ShllDeYyDerZFvXSRjiAEZZgXNPFBK4tPI/D0wvYzu08RnFKcSgRe/1KKIpjbBe4ku1L6x/EEfXU8UrrHVv0hl3rJtwJ/CUnX7V5FlXF+lmo/is1ac+Qcm8H1P6yJVWJOQoD5qd6zTGX5hOXdkFCvAuoEOXAEsr2ywUZBzjwFPAga1HYnRrvQb0tGNPj4vLPoQqL3XNOjupUiVh3EtWSXVjymWZFxa3neZuAq/XnrfojNd3/NI7jO5axjTuAzy5zlz8Zx/G9J9heHvhZ4F+iAkrSwDjwP4FPxXH8z8vcl8PhcDgcDscKMK1E0kF4gUsAOMwmBpmkqFvbSEgyqDZBkmdri9t2M00U+kShT3t9mpKOe+2nQa2nSGXbHEcKm2B/VuWNgnIUp6xhW2iGGAdXpoEVAuzRWUG5K+QYqwKxrFPA5MnaiKNJw5o5D5SPPw7Zln1c0oonO7TM0OTFmNMfLa7Cu9UxHYTs+2bJF5TTODtVNodYB7Ip1dpHjinAysHVQr3kGVFeRem6R2W/z3P+CFu596OYNxA/pqfNwGgZrkeHZGuJIL+1h4AwRgncDcCRs3XQp0VEwDpXUOqcsdJXdFUlCXiedwlKxG7rmrVdf+70PO/H4zj+27N+cA6Hw+FwOBwOh2NNE+GzzoUlnzPO5uuCcVTn7B85jW28C3TztsWZWGqG53kF4AGMsP0T4D7UK7l3Av8BWA/8led5PxTH8VOncZwOh8PhcDgcp86BT8GB9/Pdwg8D8EN//HeMM0yLDCVe6XB80rRokE8cW1/biGHoJwWNpoP+xBjL06BElRAff3PITLaPTLYFQH3PRvU0NYXJBQWr363GLvQkvWcFyTMNUP1is3qZIUzobdbahl2tWfJQgcXdy3mYSClnVtxZqeocWuN2mDOjqEJRp0nlCXh0F9wGmWwb31fu+bogYqE0r847cY91ZeqkerLqc7ugrzMlbTdLWPIEMCGPnk+f/rGeMyRP9ko9PoIKQe9Xt+F6YL+edUV5cee+ifq93QEc9GDf7VCd5rUlwOrBVUs+t6y0uP114HvA9+I4nvY8bwQ4dBrb+34cx2OnuO5dmHbbvxzH8e9a877red43gb9H1bf/JPDDp3yUDofD4XA4HKfFLPAYfHoIgH/47Z1E63128DQjjJGmRT4pwANFauRpkKGVFJSyiUKfRqTEb80vUqJKHzMEROTLc9R0K5zGzldZqPSYVkGP2luRHFpPCZDufNksndPBVC+W6sd2fq+IWju3V1riJGLHDnMO1Hi3IA7oFEd27u1OYM97UP7KHKfHV6C5C7ar6zkXqpBbP4hUGHIQH/9kHXrqU82yUJhXebdYx1tBWS8PzqOKWK015P5cjBKyc1C4qTMU/DbU+W5H/RZGrHn2vbbXkes4pNd5tB+i+TURmazybZcnbpebm+tYPisqbuM4/tWV3P5y8TwvBfxfevRZ4Pe6l4nj+Lue5/0P4CPAOz3P2xXH8RNn8TAdDofD4XA4LJ4GbgCgeU+Z6X/XT5kZAMrMkNFOVpBUUW5TokpFW7Sl9VWm6nloplio56lpYVUuK7swTZscjY78XfphZjcsfKlH7Vpi4ibUnhLsJ0gRmt1tekTQDnD8enYurz2etB+KOf4xNQDmoRnAmGdEUonOPFypqgxKTJc8qHYn9Z4KV0ITsiOqgnG9oq3jaup4sZ3kFGtHt4n6zmoxWEFd04dQtaP4Mqssm28Z6IrXgCokdr261yOYHG5pyyQ51PaLiCzqGkjla8n17rPmy/0sod6trAFxG+HjLTvn1onbM83rJYv5Bkzwyp/GcbywxHL3osQtwPsBJ24dDofD4XCcQ7Sbd9ctvPjQdqKv+hSp4RMlxaXSqFDXIjWqlChqK7SPGeolPaYLS4FqG9QiTZEaGVpUKNPSbYP6qEA/1D7QovnQBhM2+wBQ8ZRgFQFqF1ES7JBgCTntCFnu+pZ1RUQnGtSzVpINzKEc7XkIt5hjs0V1t8DOotoCPfTLwH/l5KsmW5TeCx+A5tSGTsdZhLXQESKdMtcra61TRV3TBwC+jioDvZboxZStBtikrvNI12JSlEzuyxSdoe5jmGsjiPNeQoUyT+lljg9IWJUsnEQroAUnbs84rxdx+3Zr+JHXWG4v8CrQg8oKcDgcDofD4TiHSK7oZpjaxcRLwzy3eRs7eJoZ7dCWmUkepgeZTJzYCJ9czxytZob5Ztr0XkW1FkrzMm3S9Gk3WKaXeAXWQ3RDwDy6xc1u4G7Uk6P0nrVzXG1xKeK2RKfg7BaBkl9r97I9jhgTTjxLUsHYzgEWp0+2bwsl2Q8pVOjsqeSzblBf1efh5q3mmO02PnL+IujlGnSLeTnP+1C5p+EhPdHu83oaAnzF6cW4tSMkj8sFjIMuvxFYJEzbmmfnV9uiV8TuFErz69ZLS/9GHA7DWhO393qedylwAeov/yAqoOOP4zh+6TXWu9QaXvLVWBzHoed5LwBv6lpnWXieN3SCRQZOMN/hcDgcDodjEb4C+w7DgVt4rvBGiutriy4V4pOnAZC0DnquvI02aaqvqiC2CD9pC5SjQZsM/RwGYIYyNYpkaDFYnuTFnSoXlwkPfhMVQrsPJS6ryU6NsLMLPYmwlWVscSItcF5TtDSAGqawlIi/fuAYNLXwlu0EdIppmSe5vpVTyblNoYL5gI8OGWFbxbjNVZSBWUUJXbvYll0wyW6tNIourDQLFFFC0Ra1gZ53rrH7FvcCm1HXJAdsNfd6O1ZLKGv17mG7f/IU5l51t2uy3d4x9DGsjfZI0Unk3Lqw5DPPWhO377CGy/pzDfDvPM/7hTiOP7PEesP6+9U4jrvr/XUzjhK3Gz3Py8Rx3DqJ4xs/iWUdDofD4XA4HA7HeUS0sA4WliluF1a2WrLneZuAq/Xnrfqj66bzp3Ec37EC+/wA8JMoPXUB6jXGt4A/iuP40dda90ywVsTtD4AvAN/FCMg3oLpA34Z6V/Rpz/PiOI7/+yLr69eOHVkhS/GqNVwATkbcOhwOh8PhcKwQT8Jdt9C8awNP37aDdMY8oviW/WlXSy5So0iNBjnCHr9jmlnXLF+gllRwbZHh4kueA6A2UmT2wGaTC3k/VtEkjGNqV0O2XVsZ73boxHVdzPVjDrQLrchhKjYXzeSKnlTVx2ZvQ5zhKig/5CAnx24Y0oF5oxjX1j5WcSMLGNfWrhhdQLm8VUz84KPA/mN6YXGUc/p7k57WT5JjfE6QEGSpiBygnNsciVVuh6bLd3fIOnSGHE91TVsM+T2EsFYcWyEKfeJwmTm3y1zuNDhrVco8z8sCfwXc2jXrYv35cc/z7o7j+DdW8jjWgrj9IurNQtw1/XvA5z3PuxUlfFPAH3ie9+U4jqe6lpU/kfYy9meL2dySSy3O8AnmD6CO2+FwOBwOh+Pk2Xc3PHA3RwsDvPC/jyaTRayq9kDqUSatH3sGmaRFmrotCDURARF+InAztMkzl/TRFaGb933ylz/PRLhVhaDW0eGiqKebACNa7EJL9rc93LSW627lAyhRG4Lu3avI6Y/VjkjWr2KEpZ13K0WchoCJrRDqUOZl5bVeCTuvMUllEkZr5xXTtd86SmDXrXnyXbHWaYK6gMc4vkVRr/4I8jg6jxK7qWUe/6mSQ2XnSdGofj09oFOIz5mwZDi+X3H3NHkJ0CFqJafaus+yzWQ56Z27NoiigDhcnsRaiM6qFBtHdY35kRXa/v/ACNtvAv8F1Zx4B/B/A5cAv+553stxHN+zQsew+sVtHMdHTzD/Ac/zfg2VCZIHPgz8Vtdi8ueRXsYuM9bwSSVnxHE88VrzPc97rdkOh8PhcDgcr4F+wH9Qfc3s1tGFPSa/FqBhvZvP0E6qKQfacrQLTkX4tMgkzm9ARJoWGdL4hMxp0ZGmTYTPxjf/M3Ov5qjv3mieIg+gHNs9dAq4bgev2TUuLXOOy7mM9cQiStjIjpZ4jrLdwsX67A7p/YyByXD7StdG5JqFqOt8DXCLWlfcaFtM24LcriDdxDjIYASxFEiasKYzovd7ECPmZQdgclxtdL4xRYzQRY/b5yCGneTMyvU8bC13jKRYFiFwpR6e0/uV62/Tq+fPq2OxHVa7sFb3fbfvfYL4VqnO05YXEh2/idQix7I6WQjXLdu5jcOVDUsGfh1lrH0vjuNpz/NGgENneiee570D+HE9+hXgR+M4lpCQ73me92VUF5qLgN/xPO/+ZaSKnhKrXtwukz8BfgP1r947OF7cSuxNgRPTYw0vJ4zZ4XA4HA6H4+xR/Rwc+CD1vRsBqI8W8TdHtEiTJ58Ui4rwaet39v4ifVQifBrkifA73v4HROSYY44cOR0W3CJDpItV5XsabPqxw4mIrt5QovngBuXoVtCFkjCCxhY43W2DFismhEenc3kC7JZEEiItSJ/VArqtzC697AbUM3gKJdau1ivUSETtFZgKwLL9xXrpyjHIPBG7oISxHJddHThAn+MMSmCKABVEyIl7KjuR4RydfXe6VaW49GW9fRHDIxjh26/3M6a3Jw6trC8ej2xzXi8v9ybVKUi7Q5PB3HP5Lcg1Agi9zu0UrPVlWKZXgQU6o9RXKVHo4y1b3K5sWHIcx7+6ojsw/LL+joCfsYStHEfF87yPAf8fKg/3w8DvrcSBnBfiNo7jw57nVYCNHP+KC9R7smuAHs/zSid4UyChxUdOspiUw+FwOBwOx1ngIOz7HOz5oBotZZnpK5PJtIgIqGlhI/1wfcKOPFwh1HOVCFbyVkSuT0iaNqEWsBlatEl3VHe9QIvo4voaM7fNMfvgZiVuJTzZbvkiT5zdebhCtmtZu33QUnQLKxGxdiuerDW+G91XFhjdAvuuQwm/FImDeUPerDOECXm2j9nONaZreEgPi9tbwoQkb0e52x3rbtHfcxhBO0+nS5myvuf1sXa7vDWMO2sL1Zy1bfl0N/fYTOfLhBTKVbWdYDCCWecJF1KdYt1uCSWIsLWd226tbt9He76Ee1eAZoxxelc3Yejjza8OcXs28DyvANyoR7/+GpGsX0CFC/SiSpCviLhdcS/8LPJaMb/PWMPbl9yA5wWoeHBQMekOh8PhcDgcDofDsSziyGchCpb1iaO1L25RYQ+S1vnIUgvFcdxGlVIDuNrzvBWJMz8vnFtd5lrKWk8ussgea/gdmAvbzVWYsORvn5mjczgcDofD4TjT1ODjOnXuvi0c/dwArZE0g+tfTvrctkknRaUigo6KyhFB4sS2OsqNgI9PmhY+ERm9fptM0hNX3F7bIR72x8m/u8H0TD/zWe0CBsBDKOdTKhqL01fHhOxifXe7d91hzPKdpbMycx+mz263cyuhxdsxkbwTwK27jCsr+xmis1etHTItDrH08rWLRZUwvW7tp+sCFLYfof7oRrVPO5R5RO+rIk7qYf0txaK6rdDuvFPb1raXoWu5eTrLyHT3DS5j8mjt7Ra71jvWud16b2cocXdOtc1i4cvi9pbojKiWeRWs6+/x2j7WKiLyYbmO7Pkhbi+1hg8suZSZ/yOoO7yVTgPyjHBeiFvg32J+8Yu9MXgYOAqsBz7ked7vLFJ9GeAOa/iLZ/IAHQ6Hw+FwOM4c0yTPkfUt8Cg02cD4UMBgWb3nj/CTnFklUlXQ8WL5t0KDPBldgMpeToZ9Ih3o7HdUVL6AKkVqpMttfnC9DoKbyMK1wF69kT6MmJyiU+jZw2qjRsTaVZXtfFdbMInglHZEJWsdW/DaubBZfTx9LC6mpzDiS7Y1oMcHrOVEpOviVetKr5LOqms4vH5cVbC+8QX+sXmtiQjej6kyPZGC+7fA1JC1o6cxbYBE9UlxrRSLN/RIYcKSRahKhWVQQjW05s3q7fViwrOFAPUbW0oqSIi0Xqc7b7pbqMqLje7pcm/s8OQqVkGpGAJPXSu7jdBqJjwJcdu53MCJis+eqHjtOcLuFnOi4xu3hod5vYlbXdHrgjiO/+E1lrkV+I96tAl8tnuZOI7bnud9Si93KXAX8Ltd2/khVHIzwCNxHLuWPQ6Hw+FwOFYxY/rrEPzmFrgb5oNepgOlIAbXv0yDfEdPW1AFo0L85NtuBeQTEloFpmR6noYuQKUeHSMC2i1ZKk2QiZJlr9j8NACtzWmeL7xZLVLRh2vnU4qba5uTtqiV/rT2OiKEChiBWbemSc6rCNI+a3sDdLaokW3Z+6/rfUreLda2Qmt7gTW90KQwVKPcM5Nca/uab2KaOfJc/e5HePwJXa355nnecPH3+cGLb2TD0GFm79wM+7VQ/BVgYhQlLmsY91SErWDn5lpFngjp7Oeb0tuawwhd2V4vJvixWxZ0u8FS3EqE7TyEep5UiO6+njJe5/jN2/nQJTpzm+uoAmAFz+Qwz7E2xG3kWcWylrGsYTnaYzXa13aPsRMV433VGl5Ood+TZkXFred516N+moIdQDLqed4d9vJxHN/btYkR4Jue530XVdJuHypmwwPeANymP3Kj74rj+KUlDud3gduBN6JKUI8C96H+VN6J6r8kDbx+Ybnn6HA4HA6Hw3FukPDVF2FiBOoe7IdmSbXvmcmWSWfauhpyu6PdjxKiraSashSKinRIco0iGR2aLORp0CBvhHCgWwqFPrVWkXRG7SPSqq9IjQ3bX4LtMDuxCcIAPq0f2STkVNw8+4l0ACV0pK1PxZouQkcEKBhx1MS4tiN6nvS8tUVpN3aobAkYiaHqHV/d2eqdmx2dJV9QonPQnyRPgxLV5KWBVKxukKePGXwO4xOxa9cT6t5QpkiNay5+jDwN9r75KopvVoJ4T3gTjOThA1tgah5zn0WYivNqO7R2ReM5jCC2++hKwSlBWg31dk0X8ZqnU0BLj2GrYrIdmi276Ba38iKginmpIMsWMJWkZZ0SKpZyDHPvsvpUvoZj9WHHXbRPsKxdrHex8IPTZqWd2zuBDy0x7zr9sbl3iWV/SH+WogH8YhzH/32pBeI4rnme927gb1Ex3v9Wf2yOAT8Rx/G+19iXw+FwOBwOxypARMvDwBjsv0M5W0PqWfNofYDswCxRwaflZ45zcCV/FlRosSBiuGXNl6rLGVrJsg1fiego9IlCn7mw81nV74lI+y3yzOFfHKn+uHduNIc8gXFnBQk5lpxXCTNGT7/KWg5rugjhK1CCVB63h2LQTjahtVLdM9spzJMqmNzS+WaadUMRC5WeTqFmHVcQROR9FfJdZiapLp0jJM9ckvecpk2NImnaiQAG9aLAJ+JCJsnQ5jKe4RX9UmDyQ4PUKHLkExfBJ1KwT5xV6TErYjeFaiWUHDkmDHmxasu9+tOwxjfob7sNkO0U2/fUMg3lhUJ3T2E7r1qm2dWn7T7E8oKignn5AOrFhNzrAT2/TqcsWs2cqMp397KGt7I2vOlu7DueXnIphZ3gP7fkUqfBqg5LRjX7/SBK2F4FXIjJjHgF+CfgG8A9cRwfXmojQhzHBz3Pewvws8C/RLnKaVT8998C/yWO4xdX4DwcldQ9UwAAIABJREFUDofD4XA4VpAAvgTcgIlIzUKzcgGZrC4K5UvOra8zcFsERMcVlIoIkpxa2+1NY8KUI6IkN3cuzBHq3MHIEpAzlCn1VInwGeUg9MChbSNquW0BR759EQzNw1TK9MYtoUSvhCPb/U930hn+irXOQdRTXXYeSpAtKSHfbmZYkLzGIDI5jlkj/tZl2/haAGeybVpBSBQG5EeO0BrIEOl1ku1MZWk1MxR79D5Ik4bE6Z5mU8eLhMP00yJNmwxlLUZHOESeOUpENAiYocyIDjP/Wf6IcYb585/4CY6EF8Fv6w0dkJ68vSiBewyDbZXaglQELyiRezEmDLmBaUVkI9vqioC1Q71FtHa39xGXVuROd3EvCS0HIwIlxFzCzK/A3Hf5DUxwfF7vaiVi+eK2M/19apXm1J4I+63ZiUKNe6zhE4UwnxIrKm7jOL6DziJNJ7t+Dfhz/TkjxHH8KvA7+uNwOBwOh8PhcDgcZ4ZTd27XKrYgH8KUkFsMu/jU+JJLnQar3bl1OBwOh8PhcJyQF6HyFOx/k2p5Ayq8s+DRqOfJZFuJY5b2lWMbEeiPT0tHEwbaSgrJJW4tSCuhiLR2a0N8WjpP0w8iwtBPXFtxOht1Fbac65ljkkH6mWZQd2ycI0/fdRVaZJgsXUhzwMr5HE1BQee91qFw1RG1z9BXzmozTRBYlZyDiGjIT9xje14bdPVa/cirHdp1pVdZCH1S2TaZbCvJH/aDiEB//CCiWKqR9lvJNTjy4oUAzE/1UimreOkWGUq8Qh+VxKEd1zZnlQtok05CuaV90tPRDkb8QwRElJkhrdcEKFFlmHF+lj/isx/6SV6c0Dd0Cvh0L4SH6MyrBePWbqWzwvIx4CWUrS1utb7WA+r6JpWIxRltep0Fvqxc4w4nvRtxWqV6NXQ6uuqCHO/2jgDXY8LPJRTddnb7gHVL7He1YRekXs6yax+74vH2JZfqnN9d9eyM4cStw+FwOBwOx3nBGBz4KlQ+pkb3A1WIrlXCz9fiby7Mk84oISWtfQS7xU9EkIQsq5DbMAlLbluhzFHom/Df0FfhvwChTwNoNTNQhmn6E3Gcoa3Fcpvh9SFz65UQbkR52n1pwtCnGW6AAROLWlyvohjTmTZR5CciOgrV+bWbGSV0w4D5eletmiCEICJbaHRMzmTbSc4wQK1aZKHaA0FMtu8V2s0M5X4VSpxUh85CdshUHVa5yCq3tkKZOfJMRyaHdXaqrK5LM5WImXV9r/JM9jLyhTle8UtJP2GAUV4gR4M8c/wkn+UP7vpFAI4eHGDjf/1njrxli/LKKjEmdHga2KDCrZt2vm0vBEMmL1laGamb3VnhWESr5Cx3588W6CwcZX/brXzsatUBSpRLwagqJvxYQpG3y7BShOuybRaa+lpXU2p7VWCZ3XXOORHd4cavveza53uQ9Bp7ByaQvgPP89Ko5mAA34vj+ETFp04JJ24dDofD4XA41jzzwJNAL3xaT/ptoA4L9TxBoZEIOD+IiCJVZEpyaiPrkTCKfHy/86m7RQafqEOEidhrNbXQFYfUyrtdCCMWQp+Z6TLtUjoR1XkaidAVVxiUqxwFPn4QkR99Cd83ub1yHO1WOjkX2f98Mw3NDAuhB4FVRCqIWKfFdt4Stq2myqVt1vPQzIDuS0s1qwWbRzMoQhBx5KVNAGo7QcTGy1+mj0ritEpxqAZ5xiKVw1qrKnd0Xn8Teh1CcaHSQ7OkBXW4hWKpxqCvXO1JBpP82xwNfj7zKQDqlxd5jm189RMXqXvc9Ez5ob1aTI+ydNsdEZx2OyQpmGXXu7XXLXQN2wLYnl+is72SFAkTgSxFoUZQObVyTLuBwnySIw06b1vniS8UAnWeo5huR6ud11lYsi7a+w3gFmC353lDS+QOvx9zF7+4UsfjxK3D4XA4HA7HecMxQIkhfuX/hLuAPo96tsj6PqU4fCtsNzqJR0HTCzeiSI2qVkt+ECpxCUZU2jQzLABHQz85BruGVaQ9YdCFq7Qzq/YVdsyrHS0oZ7aZNsWhgkgJVNACK+wQS6AKSzXqeeMIghbinu6rmjXrC5WUckKTFjWvMrB5kn4OU6RGWwdtVygTEHFwZpT5sV7om1eOI3SKxpIVqxoGoN1mgEY9x1PrdwCwje9T1v2PSrxCXocf55lTbveNLaIbAya5kCcfuV5fA1SDS3FdpX1SFeXyJj15OZ7F2vdgTbMLQsmnu9gXqNDhut53aK0j+y2hCp5p7y41qgpi5QsN7bhLOHtAWr9sCAoN6tWiuT9rgVMvKLUq0a1bP6tHfy2O47sXWewTKHEbAH/ked774zhOzs7zvD7gP+vRKnDPSh2vE7cOh8PhcDgc5xVijnwevnS7EjefgKNTqkru+oEZchkV9togRzvKJE6tiMpufMJEzEkl4GJGfaczGeWASjViSzyr8VCJuWaGoxWlsrKFBlHBP26/c5Yjm860CYhM66EoT6DDjmlmTP5sELGgxVCqpBw/O+82DJUTPF/PmZDd7rDaxRABl1WiNJ1tJT1s8zQY0810q1zA3Ks55qd6lbA7mDKht1IhuDCvqzJLbq92zLXLHQQR1aO6FdD6C+mjAkx2uNZp2hSpcRnPMkOZMhWCd6jt1ShyyXUvsJddBERMfHurWukAcD+m7Y7tKsq49BpeTPh2a0q5Jn3WsEyX7ditmEQA9wG3AiPzrB9QYd7i4ksEgIksCMkX5pLfRq5njiPVi1aotu4KsIqcW8/zrkf53kKfNTyqhWtCHMf3nsp+4jj+O8/z7gM+ALwX+LrneZ8EJoEdwP8DXKQX/5U4jl85lf0sByduHQ6Hw+FwOBwOh+NMsLqc2zuBDy0x7zr9sbn3NPb1f6DerP0L4J36Y7MA/EYcx585jX2cECduHQ6Hw+FwOM4rxszgwd+HgzkIfhpuU5Nq1xYZ7J/URYsaNPw8DV1tV4pLJWHB4qwmj4zKZStRTRzccYZJZdvMV3p1vqspKGXn36oQYLXddjOTFHPqJikUFfhU6mXlCgNMZVXIrw4nXldQzl8620p6+baaadrNjApltVks7NgultT9RBwsMg3lnoqDLY7ykZc2QSWrwo6viGAiu2hP1oXQp1gSt7tNH5WkgFeDHBfoRNUSVV6hRIEac+ST65ymTZoWGVpJ1elh3U1lhr4Od/2G6x4GYN91O6l+uMTE/9yqzudeOnNubexxcVwlBFmmidMrLq/dz1ac251d290O7J6HIGJ9X5VcxuQ+y+/K9yMKum9wnjnyqGUa5KlGJeWIz7I2WEXO7dkkjuM54N2e5/04qhXsm1HB6NPAt4A/jOP4uyt9HE7cOhwOh8PhcJy3qLxGPjcNE6ro0MLP9eD/mGoC1M84NYpJ/qwI3XaU6agi7AeqqnIbJUSK1Ex4MjXCss+U5MGKYBVhK3mtkIQFL4QBLS1K7RzgdjOdVD6u6xBmKjqUuArUdfXcvnmTl2mtnwhh+xjquqKwiLNu7OndbWogEevtZgbWqxDgIjX6UOG1MwMz1LJF/CBU+cADfmfObXaeVGGOTLbFYEaJUl+3AKpRSHJq5cVCjoZuJ6Ragsp+cjToY4YcRhzKPcgzR4RPiSpz5JP86DwNnmMbpXdXaZHm+fDNsEevXEeFrGetcfvcu6+V5NvaObqyrLQUKqCCYEX8DsC6kVcplmqJqG1HKs847SuhLuHtEoKdpk2En3xmJzapyt8rFsh6hllFzm0cx3eghObpbONeTsLRjeP4L4C/OJ19ng5O3DocDofD4XCc1xwD9sCeH1OjTdi/8yo2XjLONP2UqSSVi9O06GeaSX+Qlp9mrqXEou2wNvw8VUqJsHojzzHOMIeDfhbAFHcCJWxtxOkLvMRttSsz+0FErmcOf32V6tESfetnmNC5rZTMY+v6gZlEFEehnzi2vNb+pR0NdIqPxRxMqThcIBHKfla5tuIqSrXkEf8Q389uA1RrIb9UIxpQ6xytlNgwMEPJr9LPdCJM+5ihQZ4MLUJ86hRNbjH5xIFN0+ZlBgEoM8MLjHIJB7mAKmnaSNksEciBLvYl4rZElTQtitTZx06Cd0cU363Wevwb71Ar70Xl5kp9W1voF+jMH5ZCVejrI4WqBvT4CAy9+3lqLeWclzJVfewV5sgT4pPxZ5Jjq1HEJyQgImf17a1RYJJBpl4chv0peJDOtr4OxxI4cetwOBwOh8Nx3vM0hFq17L0dbvU48psXQQmq108lIkRa0KRpEeEnRX9AOWstMkkbn5IOo43wydBmpH+MyaMXEmXNOvP1nHJPC7owUlLV2KNRz1Ms1TraArUzavshPtvWfx+AxpAS2HlfTbcLYIESx61mOhG7C1LAShBndqlw0e6n4e7Q5UC5sFG2TSPKU/KrVCl1CMjhnnFeoUREoHoC6+Mrb54hwqePCiWqyTVLjp2IafppkCPQ23uFEnOtPFHok862KfpKjDbIU6LKDH1EBJR4paNHsRTfCrTnKQwzTkTANTzGNTyWiPLbb/w8f8/bmbsxz/964b1wQL8IeAAl7m/Q10IOOQtcj3J+S8BVwHZdAbqZYsPoS5T9CnnmeGPmueSY0rRpkKNElT5maGnhHhEwyGRyDjWKyXmORVuYPbhZ+YV7gUdRGZtrgXn9We6yjjOKE7cOh8PhcDgcrwueVV/h83DgKbjtTbB7K0c/OMDRK5Q9N7xrnH6mKVLjMJuSNUV4hPikabGFsUSQilsI0F6fpnJUVWUOkvYuRuz6Oud0vplmuDyeCNugKz4zx5x2IEN2+v8AQJUSGdqJcywiqR1lOkKTF3Q/WkCLaQ+yMWS9ztBbO/fUzpG180hB5RFrin6NFmnaZBJRNk0/eRq6X6/0DVbz5CVBjSIXMsm0vqZ55qhR5AUuodYq0mqmk8rJfhDSbmbwg4j6xEZmC+qACqUa9Khtb+M5fc3VtZ0jr/en3XBL9PqWWg/xyVnjb+M7jDNM6ZI/o3KJvm/vVuL4O623Uc7MJOHCz750mapMvb2HN2z7J8ZnhhksTybn2ccMZWa0I6vikovU8QkpaTE+R56Sda8b5JPw45buD7Xv1Z3UJzYqEX0/qpdvPQbMfVjVRCw/3HgNtAJaazhx63A4HA6Hw/G64s+BHDCkwlHvAz6q5uzhJt6861FAObUiajdxmAifMjOq1yrtJBTWJ2SYcZ5jGzkaDK9Xobc1ihR6aonoK1JLCggNM06bNDkaibsHJIWtAlSOr6wHcAFVQnyqXIBPmGyr5hdp9Kg84bbdxxaIgoiFJA84gIJ2J0M6W+HYLW8KevqAttX+//buPs6uqr73+OeXOZOZTCYwkIFEEmyAoIGiKIKARcBe2qJ4b9G2iu29XooP1VtpfWirtU/0yd5afVWvzw9c0eqt9QFFsfiAGBQqFUSqkYQwwGAm6QSGMCHDZCZzJuv+sdY6e52d8zSTOTnnzHzfr9d+nX32XnvtPXtnJud31lq/FYLO2Mo8SR/7ZleVumpPTy3n0Ynj6e49QE/vdKlFGnziqGmWMz49wIqeSR7Bj3tezgEe/OHPw4DLWkxDF9+ZKWAQDsb5Ytf7i5sY6aW4qYvZo7sYYiP7WFXq5tzHfpYzzQF6ylqIu0LKqnjO9O7EVviNPMDT2V7avpvjmaXAs3ruYRdP4bEwe8zz1v0bu1lD1xofHG9Y/VBpPDBQGvPbxWyYyigLtuOZVzBZCsR9OrMVbOfp7GMVIzv9GGO29fqgdjM+sO0HpgycdUYw2EZjbpciBbciIiIiIiILYYlmS24XCm5FRERElpz9wE0wcpNPJDR8jd/8BviPzef56Vw2OPrX+ha4h4obeObRPwEotc7F7q49HOAAPZzOvZzALsbw3Vtj19t99DNLgVXsKxtz6uvxdRwIXVLjuN4o313ZH/c4fUyWMjyDbzUsdM3St3KSyd6+slbVQmGWYrHLZzKOLbtJ0qnugX30hMzLUf/Kfeyf7mNVz76kq2+RIl3+Z++aPWQao5mJFX46pN4Z9m7z3bz3Tqz1XaEn4O6htVkCplFgEBi3Q7MUD4bXmJF4ODvH1MSxjGxcxUhxI8v6JzllzRAA//nkCTxt5X30sZ9JVvBYeAa+lX2aNTwSWs67KCat4/7eL6dIV1kLuT99FwM8Xno20yznKewqJb+KUxilx2TnPFB6LrGumMV5V0iQNUsXk/Sx9YFn0z2wD+4Izec34/9NDiT3II6bHqX9qeW2pRTcioiIiCx1267xr2/4Exjs9smE+o2Jy4/z2zfAjzc8gzVHP0IxjI+MXU1jV2Xw3U43JommfLfXIabpKQVX4AOltFvzbFifZAVdoUtyOiWMP6YndFWeLUtotYLJUjflfazyCaa6QrbhkBBrdraLA1PLWb06S/cbkz/FJEz+WmZL5+jpOVB6H3+eLmZLP/chc/SOdfvkS2PdMBS2DeAzDA+H973J6zhZEBSzM4MP7GIm4v5k+1TYt60bNsJBVnL/2jP9vnG4G/+sjrvoZ6Vxsiewi372sYMTOZ176Qn3FLJ5emO38PFwoj4mk2cwnUwrlGVkTu9L+mzSbfF+7qOfA/QwzgC7OKEU3D78wNNDUGvMbDkqy8QcA9q1ZPPnjtA5CaXUcttSCm5FREREJPgQjP0WfKEI/Wuy6WEugam1x/Lw4LE8vMmxrH+S1Wv8eMuNDJVmJd3AQ2WJpo7nEfaHTLk+M7BPqHRMeJ8PYOO+SfpK432jLopMsqLUChz1cIAeDpAmrooBaAzgZrsK9KzMWmen6SkFxGN7n1LaPni0/5lmw2jVmIE4nicGuLOzXUyGOXUPjq30wekYPqgdI0tKNYrfF9/H+V/zn8B7KQ9i43RExVzZQti+JdS1Lal3ClgLj976VFjvky+NFDZAscCpp/yY/fQxzXJO4D8B33Lu5y5eTn8yb3EcCx3Hxsbt8RlHXcyWknrtoz88A29/aNF9nAEKzLKPVdzL6QA8/B+bfKFxmLnnqHDjKZ9juD/8nL1krdxFSKb4bV9quW0pBbciIiIiEuwB3gccCxPPhG0X+82bgUvx3ZW3GAdZyaODKwF4dPCp9F/yKCtW7ufWB36Fi075BuC7GJ/IjlLiowP0lFrt9rObAcZZzWNl2ZJPZAc7OJE+JhlmQ1l32f30lQKucQZK883uYxWT9LGasVLLamy5nC5lNS5vhe1j0odzs6uYLRboKvhoZN/0qtKUQuDD5dhCO1H0+/aOroaJ7qyLbBEfbI5T6oJ8iF7KA9iokOxPg7tChbJRnK83Pc8Y2Ty0/cAdIUnVYDf0w/3bzuT+fjj2gp0c6PLdjGfp4ngeAXxypzifru96vIGnsKvUJRl8cqhV7GOaHn7Ic0pTE4EPgB9jdak78urQDDvBKlawv/Rlwf0/PDNrxR4hS+bVS9ZlO26LQX46lVMnUMttSym4FREREZGcPcBmGNkc3v8W3Hg33LgRNj3HByIxGLkYJu46jom1wHq4deJSv30CLviFb7Gb4xnkMSbpK7WoDrOBNexmBft5OveVgs77eHppPO0Yq9nFCeyf9gFtnJYmBso78Nl1Y0sxDJYyPGetjUW6KJZaYNNu0aVAt3+ybIxuT++BUqvszNBRWavrWvz6OL7FNAa3sYV1nCzwjJ+w84FqMXlNMzRD9cB3KnmfBntpfXE9nZc2Hhtbi9fDni+sY8/gOv9+4wxbt3XDBjh24076uvyXBT/mGYzvHWBqoo9fXHdzqaoH2MhyprmQ73Evp7NvehV7x/w/gv6QHfr4lY+wK2kJX3X0BCfxEAOM08MBlq1/koPj/ksRhsO9G8/97LGL9trwWj41cPsr0vj8tQpuF5yCWxERERERkYWgeW5bSsGtiIiIiNTxmfC6E7YNA/uA3/Kbhrp9d+VRfItmbNE9D2776S/BMNzfj+8yuz7suwMentgEl8Oto5dy9AVZGty929bSv/FRJrYcB/fg6wb2jq/NugAXKB/Hekaoe4wsGRNA/xRr1+1i9IGTWNY/6ee8BZjo9S2EA47ugX3MjISxn1MwNUT5HLix++8dZF1l066nE0m5NLsvlHdFTltc01ZYOPQTeVp/2rqXPy6a4tD5emP5eM3DZJmYAUa7S63Qe6bWsWdD2B6f4SDcMvHiLNFTAdgA90+d6e91HBMLTBR6oQgTo8eV/fxTZxxL188XS2NvS6228eeKP0+8xlS8d/FZdEogqG7JLaXgVkREREQa9ATwk7AeAt6hF8LQKujtCwGnT2bEJvNB0CBwFz5I2RAOjUHNWmAL7H2PnzonZsid2HicD5aHgNfFLEJ9PtCdwL/eEzY/Cx94Dob67yHrMrytl9EzTobz4GBhZXlwVwCGjJnBpOsxof5+skzH+eAy3104bo/Jj+J6lA844dDuxbGO9HWKctW6N8eycZxqvlx6rtGk3jRQnyIbC9uPD3qHyDI3g78va/H3JT7P+GVFAR8Ux589HjMEo8MnM7oWjn3WTroHn2BmPHyRMED2nHrJuk/nA76YYKoIPIpITQpuRURERGQehsPrh4A1MPUMfATyhN+8Ddi2Bv9xc49ftuwOx3QDl8KNG2C9ZVWOARuB94MfuLg7HIuv9567/bFDM8Az/eahHwNXh3NvpdTECH7bSAG+HusI0dggPqA6A9jEoZ+I0wzH1aRBa9ryOE7WchxbN+O0PqlYvjf3mo6xrfSaitvSwHAq2Z5PZFWtpTBNaBVboIvhutPrGQv74xy0w7lrqZT8adwft6d/HfTOZC3BA2Tjbfsp//njAv5Z9ZMmw25vypbcUgpuRUREROQw7Q5Ld7JtBlgRln34j537k/1fBc6Ckf3AzrBtA2wZDmUfoRQoV3R3eH0C+CSwCh8I7ycLiNeRZfg5llLUMbYCeHkWlIbZacoC1rQLcfqaX4/HxDlpRyuUq9TtOJ90qt4xlY6t1GU5XzZtzU0D32o/TywXEzvFOtcm+2PQnB4TpyNKf540OB4HCt1ZID6W7M935Y5fLsRgeoTOCQTVLbmlFNyKiIiIyALJp4ndTxbQ5vftAW7HB8RPJOX3VChbSRr4DlcpszNZ30MWfM8A/wJjL/XT+uSDjDTLcBQzHOe7DecDzEGy1smJ8H6KrLUzLVsp83GlMbWpSkFp2v04X2fcXqlFOD/9UBS7ZRcpvw/pWOTB5JjYbTjfJTsNWOM9yd+32CU53R7n+U3nDT5IZ1C25JZScCsiIiIiIrIQlC25pRTcioiIiEiLpC274Ls2N1PapLYHuBvGzs1aG2MLZ6UkUHnpMem8telcs3F7L+UtpVDecpm+z7fCVlKtC3Lcl26r1JKb1pPPwByvN5bLt9zmuxDnj6nUBZtc+fQ6C5TXGee+HcV3Rx4dAW4m62re5jTmtqUU3IqIiIjIErQHeKKUoRk4NNBKg9YYvFUK4NJuuem41vVk40rTwDK+j/vSMaxTuXL5MbLVrqFSMqdKn/QrZSPOi2NoSV7T64jjcdN7U2ksb3o9+YA4Jt3K35vYhXkLMHovcCv+S49a46/biMbctpSCWxERERFZgkIr7hhZS2sv2RjZWmNhK32CTgPVgWR7zPw7lisft8VzptvT85J7n2/prVS20nHx2HTsbRqcxiA2nbM3H7SW5g+mfDxurKtSYBfH6OalCaggm094hJBpG7LAtoNozG1LKbgVERERkSVqBWx7DHpX+7cXUD1Lchpc5pM6xaAuZvpNp9UZJks0lbbOxnL5IC/fJTk9b5pJuJ8suIxBa6wnHwDHa4yJokjq2IAP7ieSY+K8t5vIAv8zKJfPmFypxTh/fnJl4vWP44Na8Emk7roNH9g2GiW2EY25bSkFtyIiIiKyRP0AODdLthyD21ottJWC3/yUOWkgN4QPKEfIptMZJzdFDuVTCsVMxWk9Y7lzp63DseU1nfIn3ZdeYxr4xgB5mKxl+Tay+3EGcHGynh+fmx/Lm+8unc/IXGlu4DQzMsCNAD+hIwNbULfkFlNwKyIiIiJL2O0wfrFfHcIHYLGVMh+o5YO1/GscRxvnuo1dnifwQWRsHY0trTGwLZAFvvnW0EoBUKXxtbElNo6HjWLraEx0FY8ZD+faFrYNh+3D4fhN+Ptwee660hbeSteZT4QVg+t4fGwJnkjeD5EFt0VHx3VFTimhVEspuBUREREREVkIMzTe6NyhjdPtTMGtiIiIiCxRTwD/Dqzwb7ecCxvxrYjrOXRcKRzaMhnXYzfbdCxpmmSp2jQ5sZUvtlzGcbODlLcADlDetTgdpxtbiGPrbZqgapysC/O25JjhUM9oso9w/Hp8y+16YMBHYL0D+zgw1cNBVpZPdZS+RpUyOccsy2lirbFw/iFgy/1h42ZE5kvBrYiIiIgsYfuBrX51ZBUMnp4FqGmgmp9/tlKAG8vFY9aHZYjy4Lba9DvgA8c4DjafxTgGsMP44DPtKpxO3ZMPPqfwQeQ2yrsFjyd1xPMP4pNMnQFc7jh2/SMAdHXN8thUD/TOQH93eeKsNFtyem9i4qyYCCvtFj2VvP8ywOfCjg5vzjxI492NDzbzQsqZ2VOB3wMuA54KTOP/ZX4O+KBzbvIw6r4S+ESDxX/bOXfdfM9Vj4JbEREREVni4hyqe/zH/QI+GMy3nqZBW5qYKc1knE/aNEg2lrdSS2cMCOO2saT+mAEZfEAbW2HH8Ymf4jGDZNmY04A41hcDy3wiq/TaY10Doa4L4NgNu0rVHJhezvLeaQ4AB6e6ywP/qN/RPbCPmdGj/Pt4P2IyrHGyluLx8DN9+iHgZjo+qI3aMKGUmV0GfAY4OtncB5wTlleb2Yuccw8emStqHgW3IiIiIrLE7Qmvd8PEuTDRnSU8SlXKFJx2W853F34WPljsB7aQTXcTy1SbXiidZzd2441T9IwDOMoiozGg2J1lWc53/U0TRuXn0Y0tq/H85wGXAmtnmC12laqJ6wenlvsNAw56p0v7+wf2USx20dN7gL2FENyOUp7BeZTsvt4R128HdrJotFlCpzndAAAgAElEQVRwa2Zn4ltn+/B3/O+A7+D74l8BvAZ4OvA1MzvHOVfpX/5c/Aqwq8b+kRr7DpuCWxERERERwAe5d8O2c7NuyZWC2Pw8tPl9aYtmnG4n3Z4Gr5W6KqeBZ1lrr8N3oy7iWzpji3M3jB8L432HzmUbW25jgJkPqNIpgQCuDNvGu5ns7ysV6+mdZrZYgKlu6J2hu38/PWlwu3IfBWbZtfuEbPxuDMiL+MB2jGxs8c2TwFeTDYtEfDSNlm2+9+AD2yLwy8657yf7bjGz+4F34ju6vxn4q8M833bn3PBh1jFvy5pZuZmdZWZvN7ObzGyHmU2b2YSZbTez68zs+XOs71Izu97MRkJdI+H9pXOoo2Bmv2Nm3zWzR81sv5kNmdmHzez0uf+UIiIiIiIi+PG2c1mayMzOIZup+NpcYBu9m9Kgc95oZt3NvarmalrLrZndClxYYddy4NSw/E8z+yfg1c65AzXqMuDDwGtzu9YBLwFeYmYfBV7nnHM16lkNfA04N7frlLBcaWb/yzn3f2v+cCIiIiKySN0ErIOR9T5xb2xCySdLSsfVxtbbfJflAj6hFGSJouKxsbtwPjlVPD62eB7Suldp4O7+7P1osit2C47WJtcdyz0rvL8y93MCMxMrSofOFrs4WOwKXaoL9PVPsqJnMvyYs6xiH0W6WL3mMR4trszqmgDuCXUOAzfGGu8mi6kWkfaa5/byZL1iwifn3EEz+xS+u/Ix+GD4W02/siZpZrfkdeF1F/B54HvAz4Au4HzgLaHM/wjX8Zs16vobssD2R/im8wfwAekfAc8O+x8F/rRSBWbWBVxPFtheD3wM3//k3HDc8cBHzWync+4bc/ppRURERGQR6AZugtFXQ9F81mDIpgaqlC0ZyrsX55NQDeKD5NvCtn6yLsdTHCqdPqhkkkNTLq8O609kxdKkUZBlPi6QdVeGbCxwf6h2Y+7cAHSXguGDhVkodpVOv6Jnkh5821QXs3QxS5Euxh9LTjKF73U8hA+mt0ySZUXew6JUxEc7jZZtrthL9knghzXK3ZqsX4CC24q2AW8Hvuicy38vcUdosb0deBrwCjP7kHPue/lKzGwjPoAFuAu40Dm3P7y/08y+gn8gZwNvNbNPOOceqHA9/4OsJfmDzrnfTfb9wMxuwj/0o4D3mdnpzrkjlMNMRERERNrDDD7q2AobTs+SMw1SeVxsmu04H/im895O4Uc1kpTPj01N95U+hcZOid1hKSTvSQ7KxseWWmefhf+EvIFsuqA0No7BbfqzxXRCpamPwvmnemDKoH+G437uP0uBbTTNcno4QFehyEycbmgk/Iw3T+JbxLvJMlstUkUaH/jZ/EjjtPA6VCeu2Zasn1a1VGOuM7PT8K3AT+C/2rgZ+JBzrumZw5oW3DrnXlxn/5iZvQU/khzg1/Gtu3lvIrvOq5PANtYzaWZXA98P5d4IXF2hnj8Mr48n62k9Q2b2d/gm+VOBXwW+WOtnEBEREZHFaDelaYFit+IRfJDYW6F4fg7cSsmievFBZPp+OKyn2Y3TVtd+YMLCm258oJu+59D12FIcrysGr2vJugmDb7kdADbMlJJEAdDb7a8nThFUCD9MYRboobt/P125qGw2tN3up4+pO47NckR9Fhh2+NbaPZS1MC9WcxlLW15urR+JWZ1zruFMw2aW/oureZxz7nEzexJYCZzY6DmquChZXx2Wc4G3mNkbnXMfOcz6a2p1tuTNyfop+Z1hrO2vhrfbnHN3VKrEOXeHmd2HT2N9uZn9Xjr21sxOBWKyqH+pMUnxdfjgFuClKLgVERERWaKGYPyZsCVMa7MeHwzmu/emPYWjSmNv06A3tpheQDYHbgw6J8gCzwl8UBq3F8wHv/3JNsjG8/biW4djQH5J2D5OFqxuCPsGyAL1QpKypuBgwCr/XL3TdBWKzFJgGspabx97cjUTNx/np/i5OWwcduHNbvy44CVg/mNu72zgiNrRb7lVyXoj0/vE4DY/e3GjHsQP+/w+sCNsOxn4NXwjZi/wYTNzzrmPzvMcdbU6uF2erB+ssP8ksrG7t1bYn7oVH9yux//aPpTse36uXEXOuVEz247vKn1BnfOJiIiIyKI1A2yFofBRtH+9DxJjF978p+jY4poPDdIgsT95LZIFsdvIWm/jXLW9yTph/xTZuN9ewpy3YfsEfmxt/CScXlMvWZfk3mRbwUGx4McWF3NJcguUB73FLijMcmCqh9n+/XR1FZkOH+VH7zvZn38YH8sOx4OuDz9co3PjLAJFGg9Bm9stOe1jUDVxbyLO67SiZqnKvgR8skJi3zuBfzGzF+P/MXQD/2hmX3HOjc7jPHW1OrhNm623Vdh/Wp39VNl/GuXB7VzreRpwopmtdM49Wad8iZmtr1NkbZ39IiIiIiKy9JyDT7u1UNIO7surlsr0hNc5N7E75/bW2X+jmf0lPklwH/Aq4G/nep5GtCy4NbNlwNuSTZ+rUCzt812vj/mOZD3fV3w+9Rj+u6/76pSvdg0iIiIi0rF2hiWkEb7nv/uW1LX41tv8VEBQuSWu0hhc8J8yR0M968labkdCubX41tDYobSXbHqgmAwqNpvEetdSnjQqvZ4ByhNHFfEJovKt0OkxUwaFrEW3e3A/XYUiB6aWMz4xQF9/GOl3B761djNw12NkKXWG83dj8ZtLa2x52dG5jKltwL5kvZGuxmH+poa6MM/Hx4C/xsdYF7HYglt8oqjnhvUvOefuqlBmLn3F0xbW/ANcqHpEREREZEl5OLzOwFAI9MbJpggCH3jG5E352XrS4Lc32Q5ZFuN0HG3cFuuL7W9xip9eDk1qVcQHtoXcvtiFeRMwkOsxWsAHr9UcMo7Y0VUo0tN7gMmJPg6OrGRiJMRDdwBfB4ZH8IOIm54Ut33N0ni35CbOc+ucmzKzMfxXGjV7l5rZMWTBbVMa65xzj4TrOY5s2OmCa0lwa2YXAf87vH0EeH2VonPpKz6drOf7ii9UPfXUyy62lsYGi4uIiIhIW4jjRT8OI/8VWO8bc+N413GyJFNFsvy0qXzLbtpaGgPiGNym42rTjqUxAVQx2VdIjiF535scE1tzS9dSJfKK42tj0Buvo99X3htaafeODcBIb2ilDcd+HZjYjP/ovLly/UvF/Ftum2ErPvfQRjMr1JgOaFPumGaZS0KseTniwa2Z/Tx+0HEBH0i+zDm3u0rxufQV70nW833F8/VUmi67kXpqqteVoF56bxERERFpV7uB62HkhXDzqaXeyqUuyjHoHKM8y0q1cCIGuGkwmx6T78o8RvlctencuOnxMXkU+Pa6Atk0P1PdlbtQF6EUd8S6wnHLCr55cWp8lZ8OaLTXd6f+OrD5K6GCnfipfmROrbFNbLkNbsMHtyuB5wD/XqVcmgfp9mZciJkdj58WCGBXM84BRzi4NbOTgG/iJ/WdBV7hnKuVBXkufcVXJuv5rsf5emoFt7XqEREREZElaw/wYxheA4QpgraRtXvFsbBxqqA0UKw0NjfuG8jti/VMJdvXJ3VMUD6fbRpcxyzMJPsqSaOAKbJPxzGABhjq5uBE0hX742EO3OEv4hPfxnagPSypjMi1zFB5DphKmh/cfhn447D+21QIbkMepFeGt+PAd5p0La8la7mtNwvOvC1rVsV5ZnYCPjn4CfgZqK9yzn2pzmFpS2i9TMRpl+B8X/H51OOon3xKRERERETEO4gPWhtZGg2C58k59wPge+Htq8zs/ArF3kI2s8x7nXNl31KY2cVm5sJyXf5gM9tgZs+udR1hKqA/C2+ngE/M4ceYkyPScmtmg8C38BP5AlztnPtUA4fem6xvqlrq0P35vuL5eu5poJ4dc5kGSERERESWgm1+GX5ZeH+q76Z7NlnrapR2HYZDx8jGLsbp+NpCssTxuLGufItvf7IvzYKcttyCn8s2fw35OtM5c0fxrcL3kE1OMwLc5fAZkH+CVFGk8ebDJge3we/juxqvAL5pZu/At86uAK7At6gCbAfePY/6NwDfMbPv49Nk34PPqWT42O/XwxJbbf/AOde0jGNND27N7GjgG8DpYdPbnHMfaPDwh/B9sk+gvC94JReG150cmnf8tmT9IuCzVa51LX6OW2hSf3MRERER6WSxYesz4fUsKK6D8ef4j/WbyKb1gaxbcK1xtVAe+MZ9aeKp2PU4jtFNA9h03G6lbsgxiVTBZRmS0yzMI8m13BP2XRe33RR2PIbvfqyxtTXNpUXW1S9yuJxzPzKzlwOfxvelf0eFYtuBy5xz+yrsa9T5YalmEniTc+6jh3GOupoa3JpZH/A14Kyw6W+dc3/f6PHOOWdmN+CzKW8ys/Occ3dUOM95ZC2uNzjnXK6e7Wa2Fd/k/jIze4tzbrLCKa9M1ut1mRYRERGRJW/Iv2w7FbbNwPjqLFDchE88NYBvZU3nxs1/Cj8kwVNwyJQ8HDqGt1qdMaNyf24bZHPoDuOD221hexyUV/wxfkztj8OGFSiwbcAMjecEPgLBLYBz7qtm9kx8K+5l+D4GB/D/eD8PvL9KbNSIHwL/HR/Yng08hWxG5ceBnwLfBj7unHvkcH6ORlguDly4is2W45umfzlseq9z7o3zqOdp+JtSwCccv9A5tz/ZvwL4Lv5mFoHTnXP3V6jnKuDa8PYDzrk35PafAtyN/0bjAWBTjXTZ82Jm6ymNB34TpUQEIiIiItLhLsYHgOsoBbzsB14Il+CbUGKq0rWUJ5GCyulO02A1nxK1UgAc58CdSLanyaniXLmEMmP44PYustbmLTPAV8LPMKeJQ5rsCeAf45sT681SciSVf8bfAVYvxU/gRkjSBrXVz9Spmtly+89kge0twLVmdkaN8gecc9vzG0Or67uAt+ED2NvN7O/xAegpwFuBOIj5HyoFtsEngauAXwB+N3RB/hj+G4Xn4gc5H4XvSHD1Qge2IiIiIrKYbQ6vR+EDsbg+CTfvgc2v8SMPwQeY64EXUz7GFspbZvOf1NOuyUWyluD0+JhNOeona6VNg9u7wvoYfgBfMSbS3YOfaEQfheftCLXIyqGa2XI714ofds5tqFLXMnwgelWN468FXuucq9rLPSS2+lfgnCpFDgBvcM59rKErniO13IqIiIgsRd3AM/CtugBnwSXmA8/LKZ/WJx1v20t58inwLa0DSdm4LwbEacvtVPJ+FN/lOLbQ3oVvQb75IfyktSvCjp2057Q+HdRyW3dylkgttwvtiE0FdDiccwedc6/C9xG/AZ9k6kB4vQF4kXPu1bUC21DPGPA84H/hv6N6DP9r/yA+eD6rWYGtiIiIiIiINE/TuiU75xodSj2XOv8V3/J6OHUUgQ+FRURERESkyWbwqV2Gw/ub4eYNwDrov8C3xG4IuwYoTzwVW2nHyLokxxbY2NobpwyKY2tjK+8WspbgLWEZ/mHYuR+2/MS/lrpRi3S2IzLPrYiIiIiIpNmGtwJD8OkNwBoY6PabXxx2n4EfIxsD3RjMpmNq07lpC/huxpBlPB4Ox3z6x/gA9idkgWw3CmplsVFwKyIiIiLSEjPA54CLYDwEt58OGZd7+8qn+7kAH6xuwk/bM4ZPtQq+dXcqbB8hC35HbgsH7eTQzMftlAl5MZmh8THL7Ti2ubMpuBURERERaZkn8LNnRiuAc2GqgM9aHD6u3wZwFozsxPdh3g23bQjHXI+fUBfgVLKg6W40N+2Rlp+AuF5ZWUgKbkVERERE2sZ+smmF8m7Hz7YRMxungetO1M24HRRpvEVWwe1CU3ArIiIiIiKyINRy20oKbkVEREREOsYTVG6h1fjN9qCW21ZScCsiIiIiIrIgFNy2koJbERERERGRBaFuya2k4FZERERERGRBqOW2lRTcioiIiIiILAi13LbSslZfgIiIiIiIiMjhUsutiIiIiIjIglC35FZScCsiIiIiIrIg1C25lRTcioiIiIiILAi13LaSglsREREREZEFoZbbVlJwKyIiIiIisiDUcttKCm5FREREREQWhFpuW0nBrYiIiIiIyIJQy20rKbgVERERERFZEGq5bSUFtyIiIiIiIgtCLbetpOBWRERERERkQczQeHDbaDlp1LJWX4CIiIiIiMjiUJzjcmSY2VPN7F1mttXMnjSzPWb2AzP7AzPrW8DzXGpm15vZiJlNh9frzezShTpHLWq5FRERERERWaTM7DLgM8DRyeY+4JywvNrMXuSce/AwzmHAh4HX5natA14CvMTMPgq8zjnn5nueetRyKyIiIiIisiDimNtGlua33JrZmcDn8IHtBPAnwPOA/wJ8LBR7OvA1M+s/jFP9DVlg+yPgFcBzw+uPwvbXAn99GOeoSy23IiIiIiIiC6LtsiW/B99KWwR+2Tn3/WTfLWZ2P/BOYBPwZuCv5noCM9sI/FF4exdwoXNuf3h/p5l9BbgVOBt4q5l9wjn3wLx+mjrUcisiIiIiIrIg2qfl1szOAS4Ob6/NBbbRu4GtYf2NZtY9j1O9iazR9OoksAXAOTcJXB3eFoA3zuMcDVFwKyIiIiIisiDaKqHU5cn6JyoVcM4dBD4V3h5DFgw3JIy1/dXwdptz7o4q57kDuC9eVzhuwSm4FRERERERWRDt03ILPD+8Pgn8sEa5W5P1C+Z4jpPwSaPy9dQ6z3pgwxzP0xCNuRUREREREVkQbTXm9rTwOuScq3WybRWOmes58vU0cp6H5niuuhTcioiIiIiILIg9NB60PpG+WVuvp65zbqTRqzCzXmAwvK15nHPucTN7ElgJnNjoOYK0fL3r21HluAWj4FZERERERGRBfGS+B97ZQJm5jFNdlaxPNFA+BrdznQ5oLud5Mlk/nGmHqlJwe2R1Zav7WncVIiIiIiIdpeyzc1e1UlLSm6wfaKD8dHhd0cTzTCfrcz1PQxTcHlnHZasfb91ViIiIiIh0ruOAh1t9EYlRDq+b7VrgUWB2YS4HgKlkfXkD5XvC6/6apQ7vPD3J+lzP0xAFt0fW8a2+ABERERERWTghWVPD42ErOJxjq0mbuhvpArwyvDbShXm+51mZrM/1PA1RcHtkpRnCzgN2tupCZEGtJRsncQ7+2ztZHPRsFyc918VJz3Vx0nNdnObzXLvIekH+pBkXtZg456bMbAyfVGp9rbJmdgxZ4LmjVtkK0sC85nkob92e63kaouD2yEr7oe+cS8YzaV+5zHajeq6Lh57t4qTnujjpuS5Oeq6L02E813bqitwJtuLnut1oZoUa0wFtyh0zF/dWqWehz9OQZc2oVERERERERFrqtvC6EnhOjXIXJeu3z/EcDwG7KtRTyYXhdScwPMfzNETBrYiIiIiIyOLz5WT9tysVMLNlwCvD23HgO3M5gXPOATeEt5vM7Lwq5zmPrOX2hnDcglNwKyIiIiIissg4534AfC+8fZWZnV+h2FuA08L6e51zM+lOM7vYzFxYrqtyqvcAscvz+8ysbJqf8P594W0xlG8KBbciIiIiIiKL0+/jp90pAN80sz82s/PM7AVm9hHgnaHcduDd8zmBc2478K7w9mzgdjN7uZmdbWYvx3d1Pjvs/wfn3P3z/WHqUUIpERERERGRRcg596MQYH4aOAp4R4Vi24HLnHP7Kuxr1J/gpz29Cng28NkKZa4F/vQwzlGXWm5FREREREQWKefcV4FnAv+ID2Qn8eNr7wLeCjzbOTd0mOc46Jx7FXAZfgzuLvxMMbvC+xc5517tnDt4OOepRy23IiIiIiIii5hz7mHgzWGZy3GbAatXLin/r8C/zuniFpA1KVGViIiIiIiIyBGjbskiIiIiIiLS8RTcioiIiIiISMdTcCsiIiIiIiIdT8GtiIiIiIiIdDwFtyIiIiIiItLxFNyKiIiIiIhIx1NwKyIiIiIiIh1Pwa2IiIiIiIh0PAW3IiIiIiIi0vEU3IqIiIiIiEjHU3B7hJjZU83sXWa21cyeNLM9ZvYDM/sDM+tr9fUtFWZ2vJm92Mz+ysxuMrMxM3NhuW4e9V1qZteb2YiZTYfX683s0jnUUTCz3zGz75rZo2a238yGzOzDZnb6XK9pKTKzs8zs7eGZ7gjPYsLMtpvZdWb2/DnWp+faYmZ2lJldYWbvNrNbw73ba2YHzOwRM9tsZn9kZqsbrO98M/snMxs2sykz+08z+7qZXTHH67rCzL4Rjp8K9f2TmZ03v59UUmb2zuRvsjOzixs4Rr+vbSL37GotmxuoS8+1DZnZYPjbe7uZjYZns8vM/t3M/sHMzm+gDv09luZxzmlp8gJcBowDrsqyDTi51de5FJYaz8AB182hHgM+Uqe+jwBWp57VwB016pgCrmr1fWvnBbi1znOIy6eA5XqunbEAlzT4XB8FfqVOXX8OzNao4ytAb506eoGv1qhjFvizVt+3Tl6AM4GZ3H29uEZ5/b622dLg76wDNuu5dt4C/AYwVufZfLlOHfp7rKWpS8svYLEv4T/rJ8Mv2z7g7cD5wC8CH01+EbcC/a2+3sW+5P74/Qz4RvL+ujnU87fJcXcDVwDnhNe7k31/U6OOLsoDsy8ClwLPBa4GdoftRep8eF/KCzAU7tNO4D3Ar4VncR7wJmAkucf/T8+1MxZ8cPsz4JPA7wEvCc/0ecDLgM+Fe+iAaeCZVep5dfIshoCrwjP9VeCWZN+n61zPZ5Kyt4Tjzwn1DSX7Xt3qe9eJC74n2Q/CPdyd3M+Laxyj39c2W5L7+EHgjBrLSXqunbUAryQLSncD14S/02cBLwr39ZvA52vUob/HWpq+tPwCFvsCfCf8gs0A51fY/4fJL+Gft/p6F/sC/CXwYmBNeL8huf/XNVjHRrLWhTuBFbn9fWF7fO6nVKnnyuTcH6hynr1h/3ag0Or7144LcCM+2Omqsn8QuC+518/Xc23/pdrzzJW5PLnXX6ywfwB4POx/GBjMnwPfShDruLDKeS5Kynwlf23h39jDYf8eYKDV96/TFuCN4f7dC7wjud8XVymv39c2XJJ7ec08j9dzbcMFOA3f0u2A7wJH1yhbsYeU/h5rOVJLyy9gMS/4b5HiL+CHq5RZFv4zj7+E3a2+7qW0ML/g9gPJMedVKXNeUuZ9Vcr8NHnufVXKvC2p59dafb86dcF/oRHv43v1XBfPgu/14oBHK+xLvzy8osrx68lagL9apczXyFp51lcpc0Vyrre0+r500gKciO/Z5MIH12uSe3lxlWP0+9qGS3Kfrpnn8XqubbgAN8e/s+SC0jnUob/HWo7IooRSzXV5sv6JSgWccwfxYwEBjgEubvI1yWEwM8N3fwHY5py7o1K5sP2+8PbycFxaz6lATGbxL865ySqnvC5Zf+m8LloANifrp+R36rl2tCfDa2+FffFv8BPA9ZUOds6N4D+4AfySmfWn+8P7/xLefiuUr+T6cB7QM52rDwL9wCedc7fWK6zf18VJz7U9mdkmsr+B73fOjc2zKv09liNCwW1zxQytTwI/rFEu/c/8guZdjiyAk4B1Yb3eh7C4fz2+hTj1/ArlDuGcG8V3mQL92zgcy5P1gxX267l2IDM7DXhWeLstt285fnwdwPedcwdqVBWfVQ++x03quWF7Wu4Qof74Yfy5ZtZd++oFwMxehu9ZsQffstMI/b4uTnqu7ek3kvXPxxUzO8bMTm0kY73+HsuRpOC2uU4Lr0POuWKNcumHstOqlpJ2kD6fbVVLHbo//1znU8+JZrayTlmp7KJkvdL91nPtEGbWFz5QvRmf06Ar7HpvruipQCGsH+lnWgjnlxrMbIDsub3VOfdog4fq97X9/YaZ3Rem3tlnZveb2SfN7AU1jtFzbU9xWp29wFYz+y0z+w/8F1LbgTEze9DM/iLf0prQ32M5YhTcNomZ9eIHtYPP1lqVc+5xsq51JzbzuuSwpc+n5nMFdlQ5br71GP5bapkDM1uGH1sVfa5CMT3XNmZmV8b5MfF/K7cD7wbWhCLvwmfPTLXymVaqRw71TmAt8G/AtXM4Tr+v7e904Gn44QL9+AROrwRuMbMvmdnRFY7Rc21PsYv3MPA+4NPAM3NlTsKPlf++mZ1QoQ79PZYjRsFt86xK1icaKB+D22rfekl7mMtzfTJZzz/XhapH6nsTWXeoLznn7qpQRs+1M92DTzrzh875LCIJPdM2ZmYX4KcFKQKvq/D8atGzbV+TwGeB1+C7Bz8b+GX89D6PhTKXAzdU6Cqq59qejg2vm4DfBcaB1wHH47+8OAe4KZQ5A/h8+FI5pWcrR0yhfhGZpzS5Sa2xBdF0eF3RhGuRhTOX5zqdrOef60LVIzWY2UXA/w5vHwFeX6Wonmt7+zIQv5RYgU8K9jL83LefMbM3OuduzB2jZ9qmwvi7j+Jby/7ROfeTOVahZ9u+1jnnxits/5aZvQ8fBD0bP1Tk9cD/Scrouban2F27Bz/P7Qtzyb7uMrMX46fleyF+LvKXAl9IyujZyhGjltvmmUrWl1ctlYkD5Pc34Vpk4czlufYk6/nnulD1SBVm9vPAl/Bf4k0DL3PO7a5SXM+1jTnnxp1zW8Jyp3Pus865l+K7OZ6MbwW6MneYnmn7ejt+7NzP8HOPz5WebZuqEtjGfbuBXycLSq7OFdFzbU/p/fx8pSzWYeaPNCHcK2rUoWcrTaXgtnn2JeuNdIeI34w10oVZWmcuzzVNTpF/rgtVj1RgZicB38RPrzULvKLOFCN6rh3IOfdP+Oydy4D3m9kxyW490zYUphX54/D2aufck7XKV6Fn26Gccw8C3wpvN+bGZ+q5tqf0ft5UrZBz7qfAzvA2n+VYz1aOGAW3TeKcmwLiXGA1kxSED2Txl3BHrbLScmkCg3rJJ9IEBvnnOp96HPUTKCx54cPSzcAJ+Ht2lXPuS3UO03PtXDeE15X4LnFRK59ppXrEexO+xeVBoM/Mrsgv+HF70S8m++L/k/p97Wz3JuvrknU91/aU3t9Gkzgdn9uuv8dyxGjMbXNtxSdU2GhmhRrTAa7Mwy0AAAX7SURBVG3KHSPtK/1PeVPVUofuzz/XfD33NFDPjnm2ciwZZjaIbxU4OWy62jn3qQYO1XPtXOn0MT+XrG/Ht9p3sbDPtJF6isBQnbJLVewqeDLwzw2U/7Nk/SR8khj9vnY2q7Jdz7U9/ZSsJbarVsFkf/7zrv4eyxGjltvmui28rgSeU6NcOgfn7c27HFkADwG7wvpFtQoCF4bXnfgU+qnbkvWq9ZjZWvx0CqB/GzWFqSW+QTZtwduccx9o8HA9186VtvyUup455w4APwhvzw9JjKqJz2qaLHFVdCfZGMFaz3Q52XyQd4bzS3Po97WznZ6s70rW9Vzb03eT9VPqlI1fLO9MN+rvsRxJCm6b68vJ+m9XKhDSpb8yvB0HvtPsi5L5C9NVxG6Qm8zsvErlwvb4reEN+WkunHPbyb6RfJmZ9VU55ZXJer2utUtWuH9fA84Km/7WOff3jR6v59rRfiNZz2fdjX+Dj8Jn7zyEma0HLglvv+2cS8d0Ed5/O7y9JJSv5KXhPKBnWpVz7krnnNVaKE8y9YJk33CoQ7+vHcrMTgZ+Kbx90DlXCoL0XNvWV4CZsF7x7yiUZidYHd5+r0IR/T2WI8M5p6WJC/4bL4f/w3B+hf1/GPY74JpWX+9SW4ANyf2/rsFjnhaep8N/i7git39F2B6f+6lV6rkqOff7K+w/Bdgb9g8BhVbfr3Zc8OP3vpHcy/fMsx491zZa8B88e+uUeVNyrx/K30v8/IzjYf8wsDq3vwv/wS3W8YIq5/nFpMwNQFdu/yDwcNj/OHBMq+9fJy/ANcn9vrhKGf2+ttkC/Nda9wdYA9yd3O8367l2xgJ8MLmfV1TYvwr4UVLmnApl9PdYyxFZWn4Bi33Bz+c2GX7J9uGzRJ4HvAD4SPILeh+wqtXXu9gX4AL8h+a4/EHyDG7L7buyRj1/lxx3N/By4Ozwmv7n/Y4adXSFc8ayXwB+BXgu8AZgd9ge55Vr+f1rxwX4YnIPvw08A5+QptryND3X9l/Ch5/H8POhvhL4BeDM8Dv8+tw9ngYuqVLP7yTlhvC9aM4G/htwS7Lv/9W5nn9Oyt4Sjj871DeU7PudVt+7Tl9oILgN5fT72kZL+J3diZ+79hXA+cCz8C1xf4NPshnv8/eAHj3XzliA48gCxhngffjPsc/Bf17amtzrD9aoR3+PtTR9afkFLIUF/23m3uSXLb/cB2xs9XUuhQW4rsZzOGSpUc8y4No6x38cWFbnegbx41Cq1TENvKbV962dl7k8z7AM67m2/4L/oNzI89wB/FKduv4SOFijjq9Rv5V4RShXrY5Z1PtmoZ79Ncl9vbhGOf2+ttEyh9/ZLwADeq6dteDnpr6/znO5FuiuU4/+Hmtp6mLhH4k0mZn9HPD7wGX49OUH8N8ufR7fZWayhZe3ZJjZdcD/bLS88+O/atX3IuC1+EyCg/hvpu8EPuKcqzofXK6OAvAa4Dfx/3msxCfV+DbwXufnjpMqzGyuf8Qeds5tqFOnnmuLmdkp+BafF+Dv3xr8eK4pfKvLPcCNwOca+ftpZs8DfhefwX4NvnvcfwCfcM41krU31vOb+JaKM4GBcC3fw/8d/36j9Uh1ZnYN8Bfh7Qucc5vrlNfvaxsIYy4vwrfYnox/FkfhE73tAP4N+GSjvyd6ru0nTMf1euDXgVPxc80+gk/I9RHn3HcarEd/j6VpFNyKiIiIiIhIx1O2ZBEREREREel4Cm5FRERERESk4ym4FRERERERkY6n4FZEREREREQ6noJbERERERER6XgKbkVERERERKTjKbgVERERERGRjqfgVkRERERERDqeglsRERERERHpeApuRUREREREpOMpuBUREREREZGOp+BWREREREREOp6CWxEREREREel4Cm5FRERERESk4ym4FRERERERkY6n4FZEREREREQ6noJbERERERER6XgKbkVERERERKTjKbgVERERERGRjqfgVkRERERERDqeglsRERERERHpeApuRUREREREpOMpuBUREREREZGOp+BWREREREREOt7/B/1WJKHkcxrhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use IDW interpolation interpolate unstructured data into uniform grid\n",
    "myInterp, depth = pt.unstruct2grid(coordinates, unstructured['depth'], 1.0, 3)\n",
    "\n",
    "# Let's plot the resulting grid to see what the output looks like:\n",
    "plt.figure(figsize=(5,5), dpi=200)\n",
    "plt.imshow(depth, cmap='jet')\n",
    "plt.colorbar(fraction=0.018)\n",
    "plt.title('Gridded Depth Array')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's use the new function `myInterp` to grid our additional datasets. If `unstruct2grid` took a while to grid the first dataset, this function will be considerably faster than re-running that process, because it re-uses most of the results of that first function call. This function only requires as input the new unstructured data to be gridded. \n",
    "\n",
    "All of these variables will have the same grid size as the first dataset, and we assume that they have all the same coordinates. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Gridded Y-Discharge Array')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Grid other data products with new interpolation function\n",
    "stage = myInterp(np.array(unstructured['stage']))\n",
    "qx = myInterp(np.array(unstructured['qx']))\n",
    "qy = myInterp(np.array(unstructured['qy']))\n",
    "# Should be very fast compared to the first dataset!\n",
    "\n",
    "# Let's plot one of these variables to see the new grid\n",
    "plt.figure(figsize=(5,5), dpi=200)\n",
    "plt.imshow(qy, vmin=-2, vmax=2, cmap='seismic')\n",
    "plt.colorbar(fraction=0.018)\n",
    "plt.title('Gridded Y-Discharge Array')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** In all these cases, if your unstructured data does not fill the full rectangular domain, IDW interpolation may still populate those exterior regions with data. If this has potential to cause problems when routing particles, make sure to do some pre-processing on these rasters to correct those exterior regions or crop the domain. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's figure out where we want to seed our particles. If you're modeling a real domain, it may be easier to figure out a good release location by opening some GIS software and finding the coordinates of that location. Here, we will use the function `particle_track.coord2ind()` to convert your coordinates into array indices. This function requires:\n",
    "- Coordinates to be converted, as a list [] of $(x,y)$ tuples\n",
    "- The location of the lower left corner of your rasters (i.e. the origin). If you used `unstruct2grid` to generate rasters, this location will be `[(min(x), min(y))]`. Otherwise, if you're loading data from e.g. a GeoTIFF, the lower left corner will be stored in the .tif metadata and can be accessed by GIS software or gdalinfo (if the user has GDAL)\n",
    "- The dimensions of the raster, accessible via `np.shape(raster)`\n",
    "- The grid size of the raster (here $1m$)\n",
    "\n",
    "**Note:** this coordinate transform flips the orientation of the unit vectors (i.e. $y_{index} = x$ and $x_{index} = -y$) as well as returning raster indices. This is convenient for the internal functions of `particle_tools.py`, but may cause confusion with plotting or interpreting later if locations are not translated back into spatial coordinates. (Don't worry, we will convert back later!)\n",
    "\n",
    "We assume in all of these functions that the coordinates you're using are (at least locally) flat. We do not account for the curvature of the Earth in very large domains. Hopefully you are using a projected coordinate system (here we are using meters UTM), or at least willing to accept a little distortion. Note that this `coord2ind` requires units of either meters or decimal degrees. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(31, 42)]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(101, 1)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sRbddfrnVcvrpszX3advdjEj60ZL6siDtGfTL2z+2JL2uwGpF33MpOzV5Sj5FPR8k9TcFrz0GALQxuAWwFD4oKX+d6itXqiMLkVK6RzMHeZd2WrboMu37aX5jnnU+Rt3DpKSZwVQ/2HGp4vKJtheYWZHTd4u8luX067nnk5L+KVim7O0228VmVmRGO7/tvroE/VgJ+dOK71W2rYs87upQx0rYoulr5PemlLpep2tmD9H0vaHnlFL6qqbf7web2ePbB7OmBvUtzbxFEwCgAAa3AErXPu3ubbmiF5rZD69UfxbomtzznyqwfJE/xvMz2mXV+S+55y80s20F1unm25qe1axJ+sluC7cHvz+yyDZLY2bP18w+v7vD9Z5lb7fZBjTHvXDNrE/SC3NFn+mw6PisdU5a7e349FzRy1NKlxZ5aOa2uMLMzlre3s+QnzEdal8L283LF9DG7NnbF2j6dPZPpZSKhpIBANoY3AJYKm+W9LXcz+81s2etVGcWIH9vyZ8ws473sDWzn1B239O55GdiLjWzjrfPMbPv18w/9jv5kKTvtJ8PS/q7ogMgM1sze3axfRrk3+SKftvMus1I/b58WNaKMLOfkXRVrugBSW/osHip262DN85xSvurNH369ISkv+uwXP4siNMLtLuSXqTpsK2Dkv6t6Ioppc9rOiHdNPc9npfSAU3fPmq9pk9Rdszs8VrY4Pa9mr72+/mavv+tRJAUACwIg1sASyKlNK7sFLu97aIRSVeb2XvNrOP9Is2sYmZXaOasxkr4pKbTT03SR8zsabMXMrOfVDYYnJz9u9lSSjdp5j0r3xkNcM3sKZKuVvYdXZ/9+1l1NpX9YT0VRPQDkj5rZo/utI6ZPczM/o+y00DPDhb5I00PqE6T9EkzOy+/gJn1m9nvS/pFFXjtS8XMBs3smWb278oGtlP3ah2X9JxOKb1LtN3yJpUNRD9pZjOu6W3v47+h7N6pU/6gyy2n8vdv7jobfBLIn23wwZTSfPeN9+ae/3THpZZY+yBPPsH5b8zMheO1zxT4mLJb98wrNbx9e6r3t38c0fR9sPcr+/wDAOaJ+9wCWDIppTvafxBeLenhygZrL5D0AjPbK+kryv6QO6Hsj7tdyv7Amz3b9RnNnL1acimlZGYvlfRFZfcf3STpE2Z2g7LrUqvKromdupfnr0p6a4GqXynpccoSUQeVzRj+trJE36ay7TQ1+H+rpOdK6np6ZkrpU2b2ckl/3u7XpZK+ZGa3Kps9P6Rs0LdD0iMkdT0FN6W0z8x+TtIH2vVdIukmM7tW0u2S1im7F+lWZYPv10l6S4HXvhCbzOzPZpUNK7v37y5lr2f2jOu3Jf1U+7rGjsrebrN8UNK5ylKbv21mn1M2U7xG0mWaeZ/dL0r63S51fUjTp/r+vpk9XdKNymZ7p7wppXRoHv0rnZk9XNn+O6XbvW07+XtJv9l+/iAz+/6U0hcW3bmFeaOyU+6HlM2wX2dmX5R0i7LrcR+n6YMcfyXpfHWZ4e3gHZoZKCVJ71nAQQEAgCSllHjw4MFjSR/KBiOvURbSlAo+WpI+K+lHutS7O7f8nfPozxW59a6ZY9mHSbptjn6+qb3s98rnqPMMZWEy3V7/Xyv7A/rOXNnuOep9krI/vItu4/+WtLNLfS9Qdj/OTusflfS8+WzPgu/PlfN4DfnHLZL+P0lD82yvlO02q99XKZv1/sIcdX1C0ro5+leT9Ok56tmdW353rrzQ52I++1mXOv4oV8ddkmyB9XwlV89fzvrdVbnfXTmPOhf0+pQNbk/Mse3/Utk11tfkyq6YRxs3zKrvoYv9DPHgwYPHqfpg5hbAkkspjSqbcXqbshmopyib0dqmbJa2IumwssHvDZKul/QvKaXbV6bH01JK3zCzi5Wdwvp8ZbMzA8pSYD+v7I/vec0spZTubs9o/6yyaxQvUjajd5+yP+zflVL6uCTNnWMzo97PmNkFymZ7n6lsJnKHspnWUWXXoN6kbMD18ZTSDZ3qatf3PjP7T0n/s13fmZIaku5WdirmX6Rsdv6Kwp1cvJayAfdRZTOr31K2zb4g6YsppTTfCsvebrl67zOzy5WFh71I0oXKEnUPSvqypKtSSh8qUE/DzH5Q2W1iflTSxcrOJOjvuuIyMrOaZl4j/r6FvBdtfy/pke3nzzezX0nZZQ7LLqV0tZldJOnXJD1N05+Bqc//VSmlz0rz+6zO8iFNz3hfl1K6cVGdBoBTmC38/x4AADDFzK7UdBjX36aUrly53qBXmNk1mj6d+edSSoRJAcACESgFAACwAtphY5e1fzwu6R9WsDsA0PMY3AIAAKyMVyhLY5ekv09ZgjIAYIEY3AIAACwzM/s+Sb/Q/rGlYmnrAIAuCJQCAABYYma2UdltjirKbu/1bE3fxupvU3YfbADAIjC4BQAAWHrrJf16UH5Lh3IAwDxxWjIAAMDyaii79+6fSHpcSunQynYHAFYHbgUEAAAAAOh5zNwCAAAAAHoeg1sAAAAAQM9jcAsAAAAA6HkMbgEAAAAAPY/BLQAAAACg5zG4BQAAAAD0vNpKd+BUYmYDki5u/7hPUnMFuwMAAACcDKqStraffzOlNLGSnUHvYnC7vC6WdP1KdwIAAAA4ST1a0pdXuhPoTZyWDAAAAADoeczcLq99U08erSdpQEMr2RcAAABgxU1oTNfrM1M/7uu2LNANg9vl9b1rbAc0pEEbXsm+AAAAACsvzfiJTBosGKclAwAAAAB6HoNbAAAAAEDPY3ALAAAAAOh5DG4BAAAAAD2PwS0AAAAAoOcxuAUAAAAA9DwGtwAAAACAnsfgFgAAAADQ8xjcAgAAAAB6HoNbAAAAAEDPY3ALAAAAAOh5DG4BAAAAAD2PwS0AAAAAoOcxuAUAAAAA9DwGtwAAAACAnsfgFgAAAADQ8xjcAgAAAAB6Xm2lOwAAAAAAJxszq0nasUzN3Z9SaixTW6sWg1sAAAAA8HZIunuZ2jpD0p5lamvV4rRkAAAAAEDPY+YWAAAAALq47uNn6LRt1VLrvG9vU5f+0HJNDJ8aGNwCAAAAQBenbatq186+le4G5sDgFgAAAAC6aCmppVbpdaJcXHMLAAAAAOh5zNwCAAAAQBfN1FIzlTtzW3Z9YHALAAAAAF1lpyWXexoxpyWXj9OSAQAAAAA9j5lbAAAAAOgii5Mq9zTiVHJ9YOYWAAAAALAKMHMLAAAAAF00U1IzlXuNbNn1gZlbAAAAAMAqwMwtAAAAAHRBWnJvYOYWAAAAANDzmLkFAAAAgC5akpqlz9yibAxuAQAAAKALTkvuDZyWDAAAAADoeczcAgAAAEAX3AqoNzBzCwAAAADoeczcAgAAAEAXLZUfAEWgVPmYuQUAAAAA9DxmbgEAAACgi5bSEtwKiGtuy8bMLQAAAACg5zFzCwAAAABdNCU1S55obZZbHcTMLQAAAABgFWDmFgAAAAC6IC25NzC4BQAAAIAuWjI1ZaXXiXJxWjIAAAAAoOcxcwsAAAAAXbRS9ii7TpSLmVsAAAAAQM9j5hYAAAAAumguwTW3ZdcHZm4BAAAAAKsAM7cAAAAA0AUzt72BmVsAAAAAQM9j5hYAAAAAukhJaqVyZ1oTacmlY3ALAAAAAF1wWnJv4LRkAAAAAEDPY+YWAAAAALpoqqJmyfOCZdcHZm4BAAAAAKsAM7cAAAAA0EVKtgSBUlxzWzZmbgEAAAAAPY+ZWwAAAADogrTk3sDMLQAAAACg5zFzCwAAAABdNFNFzVRyWnLJ9YGZWwAAAADAKsDMLQAAAAB00ZKpVfK8YItrbkvH4BYAAAAAumgtQaAUg9vycVoyAAAAAKDnMXMLAAAAAF0QKNUb2KIAAAAAgJ7HzC0AAAAAdJEFSnHN7cmOmVsAAAAAQM9j5hYAAAAAumipombptwJinrFsbFEAAAAAQM9j5hYAAAAAuiAtuTcwuAUAAACALrJAqbJPSyZQqmwcLgAAAAAA9DxmbgEAhVSGh8Py1uhoofWtr7/Qcqk+WbhPAAAsh1YyNVPJtwIquT4wcwsAAAAAWAWYuQUAAACALppLcCugsusDM7cAAAAAgFWAmVsAAAAA6KKVKmqVfOuesusDg1sAQKB29lmuLNWq4bJ2x12urLJ2rV9ueMiVtbas9+vWm76RfYd8fYMDvo9BuJX1+yCrxgP7fBuS1AraBgAAPYHDBQAAAADQxdQ1t2U/lpKZPdLMXmtmHzezu81swsyOm9ktZnaVmT2xpHZeb2ap4OOKMtrshJlbAAAAAOiiJZV/K6BSa5vJzK6VdFnwq35JD2o/fsbM3iPpZSmlVXEfvp4Y3JrZIyU9XdITJV0kaZukuqR7JX1B0rtSSp+bR31Pl/Tzkh4jaaukfZK+JOkdKaV/K7f3AAAAALCsTm//e6+kD0j6nKS7JFUlPU7Sr7eXebGyMeELS2r34jl+f0dJ7YRO+sFtmUcdzMwk/YWygW3e6ZKeK+m5ZvYOSb+YUkpl9B8AAABAb2upolbJpxGXXd8sN0l6raQPpZRmB0pc1x47fV7S+ZJeYGZ/Pp/Jwk5SSv+92DoW46Qf3Krcow6/q+mB7dckvUXSbZLOlfQqSZe0f79P0uvKfBEAVr/K4KAri8KMOkmT/thca3y80Lq1M3a5srELd7iyoW/f79sd6HNlxx661ZWNb4wDpSae4dvZ/mUf7DS5wbdz6Hxf1gw22fiWza5s4KA/PWzgkD8uueXrvi99A/H70tixwZVV9x/zffzOkh54BgBgUVJKz5rj9/vN7NclfbRd9GPKxlk9rRcGt6UcdTCz85QNYCXpy5IuSymNtX++3sz+WdK1kh4l6dVm9jcppduW4PUAAAAA6CHNVFGz5Fv3lF3fAlyTe37uSnWiTCu+ReeSUnpWSukfg4Ht1O/3K5u9nfJjHap6paYH86/IDWyn6hmV9Ir2jzVJv7rwXgMAAADASS1/GtNS5lstm5N+cFvQNbnn7qhD+1rbH2n/eFNK6bqoknb5ze0fn9NeDwAAAMAprCVbkscKuzz3/KYyKjSzfzezA2Y2aWZ7zewaM3uNmW0so/659MJpyUXMddThbE1fu3vtHHVdK+nBknZJ2q0lTvQCAAAAcMrbMde8WkppT1mNmVlF0mtyRf9YUtVPzT3fqmwAfbmyyz6vTCldXVI7odUyuJ3rqMOFc/xeHX5/oRjcAj2nunmTK2seOFhqG5W1a12Z7dxeeP3WWh8+lSr+PzWr++N146cNu7J9Z/mv8+agr6/1sDNd2dhpvo1Wvw9m6t9+3JVJ0uRe358Tj/XLtQ75k4VSteHKBjePubLmsQFXNiEfRjWx2fd7bPuI70uf77MkDRzy22znZ32d9ac9ypUNfuV2V5Ym675sfMKX1VfF7QUBYNVqLcE1t62Z9V1fYJUyp3pfqey2qJL04ZTSlxdZ3zclfUTZ7VXvldSnbMLwRZKeJmmDpA+Z2bNTSh9fZFsd9fzgtuBRhzNyz+c64nF3h/WK9MXHlc7kI0UBAAAAYJmY2eWSfr/9415JL19klW9NKb0+KP8vSe82s19QdjvWqqR3mtl5s/OPytLzg1sVO+qQn2KJpx6mncg9XzPPvtw99yIAAAAAeklTpmbJcUXNmROxj5bk79dXMjN7qKQPKxsHTkh6fkrpgcXUmVI6PMfv/9LMHiXpZZJ2SnqepL9fTJud9PTgdh5HHfLn/8117lf+fLGhBXYNAAAAwCrRSqZWKjcAalZ995d5TW3EzM6W9ElJGyU1Jb0gpTRXHlFZ/lLZ4FbKLillcJs3z6MO47nn/R2WmZK/sGu+0+Vznca8Q8XOpwcAAACAUpjZTkmfUjZzmiS9JKX04WXswrdyz0/vuNQi9eTgdgFHHY7lns91qnE+eWSuU5hnmOtoC3cWAgLz+FzUdvnvwjTpT8ZonHuaX3fvel/hsROuyPr98a/7nhUct/IZQ2Eg1Pg2v5wkVXymkBrDvtLqhK8zVf2642f67WBVX9/AiF9usOoDpdYP+2N7Gwfj4339O3woVOTONT7oq9Hyp3gN9vn6ajV/q3Pb5F/f6GgQPFUPTiM76sOoJGlyi6/z5vN9+FffAf/f55pzHuzKtnx91JXVvn2nb7jP75/NffvCPgIAll+Wa/j4AAAgAElEQVRLldJPS24t011ZzWyLpH+XdE676BUppXcvS+O5bixHIz13n9sFHnXIDzrnCn3K/xXLNbQAAAAAepKZrZf0CUkPaRe9JqX09hXoykNyz+9dqkZ6auZ2EUcd8tPgF8yxbP73355H9wAAAACsQq1UmX3rnlLqXEpmNizpXyU9sl30ppTSm5e00c5+Ifd8ya7z7ZmZ20UedbhD00cILu+2oKTL2v/eI+nO+fQRAAAAAFaamfUryyd6fLvoT1JKr1tAPVeaWWo/Xh/8/mIzO2+OOn5B0kvbP97f7teS6ImZ28UedUgpJTO7Wlma8gVmdmlK6bqgnUs1PXN7dUopuKoOAAAAwKkkuxVQuZeNll3fLO+T9LT2809LepeZXdRl+cmU0i0LaOf7lN279jOSPi7pm5IOKBtnXiDppyT9QHvZpqRfSCn50JOSnPSD27KOOkh6q6SfU/aa32Zml+VvHmxmQ5Le1v6x0V4eAAAAAHrN83LPnyzpG3Ms/11JuxfYVlXSU9uPTg5IemlK6Z8X2EYhJ/3gViUddUgp3WJmfyjpNZIeJenzZvZmSbdJOlfSqyVd0l78D1JKt5b1AoBTUXXjRlfWOtenHU9s8beT7j8cRAlLuv/hI66sOeCPelbq/qSL5qBf98QunxLc3FR3ZTbql0tBEnHfhnFX1mwE0caSBod8avGaft/21hF/cPPwuN9ma4N77+1cc9SV9VeDJGLzr69ixU9cie771wiuI1o36N/XiabfPo2grK/q05JT0O7wsG+jEbwHtibex6Lw7rFRn6DdHPHb8fBav9yxs4dd2Y7TfPTDyF3+fa6c8GWt8aDfLb9tAADl6sVrbpfJx5Sdcvw4ZWOp7ZI2K0tHPijp65L+TdJVKSX/h0nJemFwW+ZRh9+UtE3SS5Rt/PcHy7xL0kJmhgEAAACsQi2VfxqxP7RcnhQdAV5YPVdJuqrL7/dK+uv2Y8WtisMFRaWUWimll0p6pqSrlYVMTbb/vVrSM1JKL0spLeW+BgAAAAAo2Uk/c1vWUYdZdX5M2RQ6AAAAAHTFacm9gS0KAAAAAOh5J/3MLYDyWc1/9FPLBwhVzznTLzc84MoOP3SDbyPII2r2+RMxju0OTs5o9fkySRNbgisGojt2bfWhO4PDPsCpPzgxZGTQLzdQ8+FBUeBSM6hv/YAPmZKkesuHHA0EYU+DVR8yta7f1xmFOvVXfNBQLSiLjhxPBv1rtOLjobWKf18mm34fqwbLDQbbcSx6LbWgjYZvw1p+3cEhv12jvkhSM3iN64fHXFk9CL2aWOP7c/S+ta5szw/61zxyxzpXtmmXz05ce8P9rqxx512uDABQrmaqqFnyTGvZ9YGZWwAAAADAKsDMLQAAAAB0kWRqlZyWnEquD8zcAgAAAABWAWZuAQAAAKALrrntDQxugZNcZa0PpLGd2/2CB4/EFWzb5Iomt464slTxp8YcOLvflY3u8Mul4JukMexDcxqbfTiSGv6LfWTbCb+cJBv3QVMb142Gy8421BcEMwVhT8M1HygVBSn1V30wU2QyCB6S4hCmivw2i4KiJlt+g0d9PFIwtGoxr0+SWk2/fhRctbbPB31FwVVRqFNkpN+/V4M1/z6PN/x+s6bf90WKt0UU/rU2WH+8z7eTTvNt1Ov+/TvR7/eH8S2+vuFzdrmy0/5zvW9EUu3ufa6scZ8PpAIAYLVgcAsAAAAAXbRk4QHnxdaJcjG4BQAAAIAumjI1S44rajK4LR0negMAAAAAeh4ztwAAAADQRUrln5acSq4PDG6Bk8tjLnZFzao/wWJygw96OvID28IqreFDilLVf5keeUgQIJR8yI3W+sCeWr9fd3jIh/34nkibhsdcWRTqk1Xql42CoqLQpJr519IfLFcx38so/CkMemr6r9RO19MUDS7qC4KZxoKApOg/yPGG788xDbiyaBuO1v0+NhSENUmSBdtsXbBt4+3tt+3gkG+nkfy2iQK4oiCrLYNxQFkk2t6R6L06Wh90ZdF+UgnC1hRkQu1du8aVHd/q35c7N/vlJKl2wofR7X6/72Pzrj2uLDX8ewUAwMmOwS0AAAAAdNFSRa2Sr+gsuz5wzS0AAAAAYBVg5hYAAAAAumgmU7Pka2TLrg/M3AIAAAAAVgFmboFlUN3uw56aZ+9wZfsfPuLKju/y9dU3+RCevsNRXJPUPNuHMI0MT7iyzbViQTqRDUO+jShIJwp1ivQFgVeSVG/6EJ+onSisqVL12ycKj+oYZjXLZMt/fRYNiZKkiSDsKerPaN2HR0WvOQqUmmz4tptBHyfqvi+1qn+v5nOEebjmA8Ui/VW/30XbcbDiQ6ai7VU0yXKxiZdR8NSG/lFXtq5vvFDbow0fFLVpmw/CisLNbl4fh8mN37jBlTXX++8Y6/dtp2YUMBd/xwDAqaC1BGnJZdcHBrcAAAAA0FVKFbVSuSe9ppLrA6clAwAAAABWAWZuAQAAAKCLpkzNDveuX0ydKBcztwAAAACAnsfMLVBQZXjYl23f6hcMQlcOXbrTl50fBPtsD0Jc1vognTXrfIBT35lxINRQX8MvG4T4RKIAp6G+INhHxcKaIhYsNxGEKHXSCIKZ+vr966uZL4vDqPxrHmv4/jSC62SisKZOAVVROFMUChX1sdnyZdF7FSnaRlQWBVRJ8Xt9x8RmVzY84EOm+oKApOE+v1x/EOB0rD5QqC8DVf8Z6K/4sk6iOitBOFp0LdZA0E7Unw39/jMd7bMnGv41n7HhsCuTpL2P8O3cssa/L2d9/KGubPi/73VljT33hO0AwKmglcoPgGqR01c6Zm4BAAAAAD2PmVsAAAAA6KK1BGnJZdcHZm4BAAAAAKsAM7cAAAAA0EWSqVVyunEiLbl0DG5xSotCoiTJBnxoSzpzhys7dOF6V9YY8F9U+x/jw2cGth1zZYNBcM2WtSf8ujUfFNMpuCgKEIqCjyJRkE4UphAtFwVFRaL+VYOQISkOTaoFy67pm3BlUb9HG/2uLAqPitqNAqHi8Kfi70sULLGYoKhIKwijsmDVOPAqrjNaPzJe9//lTAbrHh33n79oO0b7yVC/DzybiELHOoSqHQzaWdPv96fhmg+9Cj8vzeLhaK6+qq9vbd+4Kxuq+tfcqT/Hdg66snsuX+PKHvT1YqFzAACcTBjcAgAAAEAXzWThgd/F1olyMbgFAAAAgC4IlOoNbFEAAAAAQM9j5hYAAAAAumjJwmyNxdaJcjG4xaoUBkUFqTeVrZvD9Y9f5MOj9l/sPy5jO33oysiuo65s51AQAtPnQ2Ci8KAomCkKj+oUKBQGOxUMQ4rCmgq3EWgWPP2mvxKH2awNgn0qKtZ2JNrexyZ8mFGj6fsdvQetgkFd2bLF/kOLtu182lmoSvDez6fdqN/jk4sIV6pEYU3+/WuM+z4O1Pz+VO/wWqIQpihk7Pik30+i4Kma+e3YCD4H0XJjQRjVpv5RVzZU9eFWkvTgtQ+4sq0Dx13ZNRMPcmUHn7TblW38hG+neeBg2DYAACuBwS0AAAAAdMGtgHoD19wCAAAAAHoeM7cATjrDJya0Zd9xDY3VNTbUp/1b12h0xJ8GCgAAsBxaKb6Ea7F1olwMbgGcHFLSI27Yox+5+gY9/j9vUzX3jd+smD7/hPP08ec9VN+85PTw+mkAAACc2hjcnqKsr9+VpXocSuJX7jCwsOAs91YQDBSsX92wwS/X78NUbNDP3qU1QXjU/kO+bLNv4/BFm/xykg6f61/L6Dk+AMoG/Ovrq/qyKGgmUpcPyIlCj6I2qh3Cn6KAnKgsOho5UG24sk7BVbP1B32UfNlgta6zb96nV7zxMzrzjuB9k1RtJV322Vt12Wdv1V1nb9Sfvu7Juv3BWyVJjZbfPqMNv3+P1n1Z0WCuqKzR8O3G67qiebHg81K0zuijGq0bLReFRxUNwWrXOo9lZ/fHd7I+6ftTqfp9vlbzZRN+Nw5Dwjq2XfAzeGh8yJVFn4MoMC2qb7Dqv3P2TqxxZVsGTriyrB3/wrcP+MC7C3fd78puvOwMV9bsf7Ar23jVF8O2AWC14T63vYEtCmBFPexLe/Q7v/TPHQe2s515xyG98Zev1sOvv3uJewYAAJBpJVuSB8rF4BbAijn35r36jdd+QkNjwdRaF0NjDb3qtZ/QOTfvW6KeAQAAoNcwuAWwMlLSr/3ep+Y9sJ0yNNbQK970mcWf9wsAADCHVvtWQGU/UC4GtwBWxMVfu0dn3XFwUXWcdftBXXTDvSX1CAAAAL2MQKleUfGBJtbn374ofKayZbOvrxoc12gEAUBBqFMaHYv7OOFDk2ztWr9cxfextXbEtzPk2x7fNuirmwgCZB7iw6PGNvvXfGy3754kNdf6bTGyedSVbRj222Koz4fANIPAgKLXWQzU/MxmzeLwqEgUNBW1XQ+CmaIwnGjdKLgmCq0azAVUPefqr/vOLsAzPvJN/fclp88oi15LFB410fCfockgKKoZrBtZrknkSiUIBAvCnqL+TI4FQW3VYh1PHQKlLOhP1MeoldQseNQ6Cr2K9uNJ/572D/j9s9khgK0WhFRFJuq+neizVq8GwWPBd8RksM8en/QBelHIWydRIFWrz2+zdX3jfuUgmGtyrX/NtdN3urLGPRxwArD6pCW4RrZoSCeKY+YWwLIbOjGpR332zlLqetxnb9fQiYJJ3wAAAFi1mLkFsOw27T2harOcac5qM2nzvuPaMxLf1gkAAGCxliLdmLTk8jFzC2DZDY760yUXY6jk+gAAANB7mLkFsOzGh/11n4sxVnJ9AAAAeczc9gZmbgEsu4PbRtSslvOF3qhWdGDrmlLqAgAAQO9i5naFVAYHVLFBKUjStKhsk0//TVFa8qhPvWxu9etWJoIUzQE/+9Vc1+/bDdKOO5VXGj5xc3yTr3N8Q3CcJWimMegLm0N+ubHt/nrO5naf5tw3GCePnr7xqG87SMzdNOQTlKPloiNz0dguShiuBsnI0XJRWcdlg9zaNX1++xStL0pvrlV80nJfe7nW2oq+evmZevSnv1uozW6u/f4H6a7KRikXXB1t70YzSFBu+vcqSkaOkogjQVi5rMP70qm8SJ1RfxpRyvNh/1mzKFU5SCxOQ0F6eoftkAaCVO168JmO1u8L0omjBOVgc9mAXzd6LeOTfttU+oPXJ2lwyIeTRftJlHDZCj7U0fs8HqR014Kk5eizdrzu39NO90ncvcbfaiuq87TBI65s5y6/7oF7driyI487w5WNfJC0ZACrDzO3vYGZWwAr4tM/ekEp9Xzo2ZeUUg8AAEAnSdnBxDIfy3QHwVMKg1sAK+Km79uhPef4swrm486zN+lrD/MzRwAAADj1MLgFsDLM9M7ffqLGhxZ2dcTYUJ/++LVPjc/bBQAAKNHUacllP1AuBrcAVsx3L9ist7/lSfMe4I4N9elNv/tDuu3B25aoZwAAAOg1BEqtkMnHXiAbXK/ogM3AQR/sc2LXsCur1P2Z+pPr/PGK/mNB6EpwWCMFYShjG/2CnXJwqpNBcFGQ1xS1feiioNKgqBUFyPT7suoa3/CGtT78af2QD+CSpKFasfum9gcvsCIfXjPeLPZRi8KjItGRvigISYoDbfqrPkxnPiFVRZZrtPx2ONrw6V8fOv8SffMPTtP//oOP6Zzv7p+zre/s3qo3/PozdfN5O6QTnV/3bJNB4FI9KIuCgiLRdo3KmkEYUadlo1CoatXvE1Gdae+gKxs46l9LbTTYd3xGkVItWM43IUlq1YL+1Pzri8o0EX3HFAuU0mSwbvTdVvPb0AaC+iRNjAchXNF7HQRANRrFwsiqgz60qrmIo/f1ICxNkg5M+P831tXGXFkzOM59xtrDruy+zVtd2fGdvu2hJzzCldW+eosra43672QAOFm1tASBUh0CAbFwDG4BrLhbHrRDP/WXP6tLvnG3fvSjX9Nln79Ftdb0gKJRreia7z9fH3zWJfrqxWdyKjIAAAAcBrcATg5m+trDz9TXHn6mBo7VtfXAMQ2PTWp0qF/7Nq/ViZEOU20AAABLjFsB9QYGtwBOOidGBhjMAgAALIKZPVLS0yU9UdJFkrZJqku6V9IXJL0rpfS5ktv8SUk/K+lhkjZKul/S5yS9PaV0XZltRRjcAgAAAEAXvTZza2bXSros+FW/pAe1Hz9jZu+R9LKUkg+EmF97g5I+IOlZs351VvvxQjN7fUrpjYtpZy4MblfI8dNqmhzpC0OYjp7V51cI9v3GsC80nxOk0a0+LKQ64ds9EdwutDkQhMJ0yO8Z2eN/MXq6D10Z2H3MlQ1Vgv4c9ek16zf4AJLhAf9ZPG3kqCvrr/iNMxmEHnVSPFypWChUFFwUBddE7RYNUZKkWhB8UwnSecJAqYK3F48Cs45N+vcv2t4PHFznyqLtIEmDQ/69rhZ8X4reKD1FQWZRKFDBXSeqT5Imj/uZ6cpRvx0bwfrV8SAo6kTQx+C/qWqQoRb+31r3ha2++MVUgi+oFHwMUjNYLgqZigSLWVBfKPi8NE/E//1ZEFAXvYfV/uC1RLl4QdtRcFh/vw+nG+zzZVEw2lB/HIB3KAiUOneND20bqvgQw039/rt2cMcJ35/7/Oe3vs7/H9Z3dvAfzI03+zIAOFklKxw6OZ86l9Dp7X/vVTbo/JykuyRVJT1O0q+3l3mxsjHhCxfZ3rs0PbD9jKQ/abd9saTXSjpX0u+Y2X0ppXcusq2OGNwCAAAAwOpyk7JB5YdSSrNneK5rz9h+XtL5kl5gZn++0FOUzexyTQ+OPyrpubk2rzezf5b0FUlnSnqLmX0wpeRj+UvAfW4BAAAAoIuWbEkeSyWl9KyU0j8GA9up3+9XNns75ccW0dyr2v82Jf2P2W2223p1+8eNkl66iLa6YnALAAAAAKeea3LPz11IBWa2RtJT2j/+e0ppT4dF/0nS1HWDz1tIW0UwuAUAAACALqYCpcp+rLD+3PNioTHeYyRNBYlc22mhdmDVVFryY8wsCBlaPK65XSH1tSatNaVKEEoSHHKIgqLGt/n0kvpmH0CiIHAnCk2J9A/GQSWR6rl+2b5Jv98OBCEpa4JQqLM3HXRl0ZdAFITUCIKLonVbHdKx+qu+j7UgKCo6nSSqMwp16q8EYTEt/5GMXl9UX6NDyFTU71oQrjXa6HdlEw3fn/uPrXVl0Xt69EQQKDXu94coZKjSV/z7teg+0Qq2TxRcVQ/22SjMqDFRrN+d/tuqHg7e6yDEqeh/NQPBlSt9x4PPflBfv8940+S64LupGr+aVn8QehYELkVtRydLpeB/Jgu2TcHsNjWrRePEpFQPPkdB4F1zoliiWNTF6oB/0eNN324UXNKMluvQ9lCf/04+ODniyrYMHHdl2/t9KN+6YZ9G9sC5/nNevzV4A/cf6tBLAMAKujz3/KYF1nHhPOq4SdLTlI1BHyTpWwtssyMGtwAAAADQRVqCtORZ9e0w615/l1N+583MKpJekyv6xwVWlY/Dn6t/d89aj8EtAAAAAKwy1xdYpszR9SuVnVIsSR9OKX15gfXkT+nzpwLNlL+n3JoFttcVg1sAAAAA6KKV4kuhFlvnSmjfuuf32z/ulfTyRVSXvz7FX2c4U/7G6kOLaLMjBrcAAAAA0MUynJb8aEn3l9pAwMweKunDysaBE5Ken1J6YBFV5gMZfIDLTAO552OLaLMjBrcrZHxLUmNDCoNAopCU+tYg4GgkCHAKgoa2bvBnCAwHQSMDQYjSibrfR6MwI0kaqvk6ix7higKAIlEIUyMVC3aJ24hfS38QuBQZqvjXPFT1ZSeCsKaoP4PBexCJgqyO1weCJeNl9xzb4MomG347Hj7kw2ei0KSjJwqeWVLz60b11fri7R+FQqXgbZ2c8KFQrVYQSPSA32bhHhu0kYLcqeYa//7ZaLx/VoMgrWh3jMLkol25Ffx30giOiUa7dnXCl/Uf8Y2kDtcCNYaDcK3g2G1jJOq4XzfqT/QxL3zAO3oLOn01hV8TBf+Yid6Yht9nm8Filf4gZCoIYItUq/H32GQzCNYLXngURhfV+dBN/m+ug0f8d8TkSBAcd9oW38EH9voyADh13V/mNbURMztb0ieV3Wu2KekFKaWOCccF5WMp5/qDMP+fxlynMC8Ig1sAAAAA6CItwa17yp4J7sbMdkr6lKSdyg7lviSl9OESqs4PyHdJ6nbtbj586u6OSy0C97kFAAAAgFXKzLZI+ndJ57SLXpFSendJ1ecTjy+YY9mp3zckfaek9mdgcAsAAAAAXSRll0KV+liGfpvZekmfkPSQdtFrUkpvL7GJ6zUdJHV5p4XMrF/SpVPrpJTmCp9aEAa3AAAAALDKmNmwpH+V9Mh20ZtSSm8us42U0jFJ/9H+8almtqvDos+TtK79vIzToUNcc7tC+s89pr4tpnVD4+53USjU/uM+tGPLmhOuLAqFqgb1DdeKHSyJlut0vUEUwhSFl1SC41SNtPDjLINWLISpFvRvQ18c1FY04Gqs6YNTJlv+YzUYhEwV1Qq2TfQeRKEwkjTe8H08fNwnDbWawfrH/brNaNtETQfhUVEeUQoCd1qVePufODFXCF/bhK+zEpRt/rrv0MT6Yte/NIZ9mSXfv8m18WsJN+OEbzsMrur3K0+u88v1B5+/VpjVFQU9+aKBIGRKkiwIXGpF/Q4yz4pebVQbD9oI/gdrDgVBWEFomTrsY2F4VLRoGDxV8HusEbwWC1Kvgq+NShC2FoWqSfH3xETTb7ShQd9Q9D0dfd81g8/v+OYoLC0OvcLSqAz7Lygb8X9HKAXf0zW/jzT3HwjbSdF9TOIvGaDntWTh37WLrXOptGdKPyzp8e2iP0kpvW4B9Vwp6W/aP74hpfT6YLE/lPRDysaWbzez56WUvvdl0D4tempQfVjSO+fbj6IY3AIAAADA6vI+SU9rP/+0pHeZ2UVdlp9MKd2ykIZSSp82s/dL+klJPyzp383srZLulXSxpN+UdGZ78deklA4tpJ0iGNwCAAAAQBfLcJ/bsj0v9/zJkr4xx/LflbR7Ee29RNlpx8+Q9KT2I68l6Y0ppb9cRBtz4ppbAAAAAMCCpZTGUkrPlPQiZcnMe5UFTd0t6b2SntDhlOZSMXMLAAAAAF20luA+t2XXl5dKmhZOKV0l6ap5LP9eZYPZFcHgdoUM9tfV31/XYM2HIUU7+obhOPhoNisYhLS2b6LQcpGaxcEgYXhJlPgSGKn64KpmUF8UrhTpC8Kj+ivFgqckqc/8+tFrifoT9TsSrXu0PujKojCqIxM+EOqeI+vDdsbHgxCYoz74yIKQm1C0WLTbBUEzCgKTrB68z1EakaQoc2fk7qCdoGjHF0ZdWd9BXza+c62vrulfYP89R1xZfbtPdaqviz8Do1t9+egOv1yUb2RRkNKA7+PEhmB7R1kvwT5bafj6rMPXRnUyCHGq+jqrQShUijZPFLYVZOC1+ooFYdlg8KI7fFWmyWAna0ZvQrBy0T8jou+Igl9P0XZtRmFwkk5M+M/5aYN+vy36x9W2wWOubP16/xk6dF7w+f1U9DkNtjVhRF1VN/jv+VQPdp7zznRFE1t8yFR1zK+bKsH3xjnBl5Ok2i17/PrHfdiljfi2W0eO+nUbxf+fBpbb1O17yq4T5eK0ZAAAAABAz2PmFgAAAAC6WYJAqfBMHixKT8/cmtlbzCzlHlcUWOfpZvZPZrbHzCba//6TmT19GboMAAAAAFgCPTtza2YPl/TKeSxvkv5C0s/P+tXpkp4r6blm9g5Jv5gSZ8ADAAAAyPTgrYBOST05uDWziqS/Utb/vZK2FVjtdzU9sP2apLdIuk3SuZJeJemS9u/3SXpdyV12apWW+qpNDdV8WFAU2LR18Lgr2ze+JqjXh3FsHvDhDn1BG/Ug4KgaBFRVOiSxROu3goSVaP1K0E4tylwJjjtEAU5RG1GAUyMFgSZSGEoz1vThLI2gzuP1AVc23vQBK1GIy74TI65sy7APbLnr4EZXVq/HryXt9SFVtUnfdv9hX9YYCkKFgoSjVAvev+NBUFSQMxMFHA0eiPexDbf7z8vAXr99jp3nQ6H6vn2X789R/7nq+7ZvI0p8iGJvancF2/r83cGSkuT7aE3/lRyFQtWDgKMomKk5GHwO/G6sehAmVomCvjr8jxHlvFWCzVgLcvEaPmcm3CeiHLuo3eZav6BVih+vTFEonxX846M/6GQU1FYN2oi6GKyags9fq0Og1M51PrBnIAjW29jn/48YtGLBPof2B/vxRBC0N+w//JWq/85Kp2igVHXzJl8Y7Hets3yw09EH+fdgck3wvbE2qK/q/78a2u93xig0TpJq289zZZV6EMB32CfC9e856MrS2Ljv4wG/nNX8l1Fq+s9fagb70ym6jwGnil49Lfl/Snq0pG9LetdcC5vZecoGsJL0ZUmPTym9P6V0fUrp/ZKe0C6XpFeb2blL0GcAAAAAPWjqVkBlP1CunhvcmtkZkt7Y/vHlym4OPJdXanqW+hUppRnzBymlUUmvaP9Yk/SrJXQVAAAAALBMem5wK+n/SVoj6W9TStfOtXD7Wtsfaf94U0rpumi5dvnN7R+f014PAAAAwClu6j63ZT9Qrp4a3JrZ8yU9S9JBSb9RcLWzlYVGSdJcg+Gp3++StHu+/QMAAACw+mSDUSv5sdKvavXpmUApM9sg6U/aP746pbSv4KoX5p7fNMey+d9fKOmOgm3M28bBMQ0NVbVr+LD73fZ+HwJSD4KPtg/45SaCxJeorGOQ0ux2W8WWk6RKkPgSBTtFoVdRcFXYRvJtNII+RgFVfUG7ncKxom12ouGTeI7WfYDQRBAKNFr3YSr33usDRCpHgnaPbXZlUZDOUBAIlVXqi4bv86970meSaOgBX2atYN11vu21e4Lt3fTrDhwKQtUOBMlDkqwVBIbcuceVjdzgQ6aWI0KkNe7DUOxb3wmXHb7bb/CRjetd2cRZfnlger0AACAASURBVD85crYPgQnyzjS5vlgoVHXCl9V9tlnHQKngoxWGh0X7YpBvFIZHReFKlSCsKQWhTn19voOd/qBINd/JFOy30Wuxqm+7OhiU1Xx/ztp8yJUdGh9yZYM1v8GqlWiDSeev2+vKHjp8j18/SNA70fL72LGm/7474/QDrmzPLT7jcWy7X3fdZh+M17g/+NLpUVHokSRVzj7TLxuEIT3wlNNc2eiOYJ+PgtWCMMA4LM0XHT/H758WBaNJqo75xqNAuIGDPjlu8IDfv6PvksFDu13Zmhv9vp0OHXFlrVH/f4E6/F1j/UHw4wkftgbg5NYzg1tl6cY7JH1BBUKkcs7IPfd/Bc90d4f1CjGzXXMs4mMOAQAAAJzUkpbgVkDRESYsSk8Mbs3sCZJeJqmh+d+HNj894u/7MVP+EJ2/z87c7p57EQAAAABA2U76wa2Z9Ut6h7KTZ/44pfTNeVaRPxdqrmTl/Ml5/nwZAAAAAKecpPiW5IutE+U66Qe3kl6r7PrXuyS9YQHr5y+CC65KmyF/kVF80V93c53KvEPS9QuoFwAAAADQxUk9uDWzCyT9r/aPr0gpLeTK/mO553OdapyPUJnrFGYnpdT1mt783YUeuf4urd84pOEgySUKj6oHSS7RchMF39JmcM1AK0iliG4u3epwfUCl4HUIUXhULUigiPoTNh0lUASOB4FQ9436AB9JOjzmJ+7H61HYkw9JUct3su8e3/b6B/xy9WAPXXuXDxrZcOMxV2bNeDtYEIZjh/36zf0+GKaybp0rax3ywTepEYTcbPRhMc1g3Ugcj9Obom0jxUEnNulPLhmo+/U3Hw6Cp7b5fTbKjzl6pg9NGd8ShNTU/MoNnzEkSarUgw9m0VCo4HyaFHyNRRl4qRK8wKgoKKtWOxwvH/Dbuz4evPAg9WpwxL+Y9SP+OOmafr/crhEfLvh9m+5yZVHYXbPDjQ8eMeLXP7fPB/FsqvogtPFgg//16BNc2XCffy1pTbANR/x+Vz/XBybZ3v2uLKs02KFO8pjRSvAdKEkPPGm7K2uM+A/H6GnFdubG+uC7v89vL4v2+ehLohmEqvmlskXX+fc61f369Y3+9U1s9vvY8L3Bdtju9/kDD9npytbe7WNNKnXf805/Mqz7wp3xL1wFwfYJvrvTRJDUh542lXBcdp0o10k9uJX0SmWzrbdLGjaznwyWuSj3/MlmNvXt9tH2YDg/4Jwr8Ck/88r1swAAAADQI072we3U4fJzJL2vwPK/lXt+trKAqG/lyi6YY/38779doD0AAAAAqx0X3faE+Fym1eUOSfe2n18+x7KXtf+9R9KdS9UhAAAAAEC5TurBbUrpypSSdXtoZsjUk3K/u7NdR5J0dfv3F5jZpVFb7fKpmdur53m7IQAAAACrVfua2zIf4prb0p3Ug9sSvVXZPXIl6W1mNiN5pf3z29o/NtrLAwAAAIBSWpoHynWyX3NbipTSLWb2h5JeI+lRkj5vZm+WdJukcyW9WtIl7cX/IKV061L3aUNtVBtrTdWDWNCJKCW4oEqQfNgXJBFXgtTEySiFMzii1BekhEpximekElxgECUj14OyQ5PDrqzWoT+zHRgfcWW33b0tXvi4fy3V474/G+/wqw7t9/1Ze7NPCba77nVlzaNH/XIDPqk1TGacxzek1fzrS0HacivoT6f039mKJiOfqqIkzbBssu7K7MBBVzZ03CeFyvznd/DrPil74qH+LmYTm/w+cuTsILK4g+hrrBokI7eiKoN1GwUTlBUkKAebQRalxKpDcmWUyhykoo+P+lT0Mzb6FOTda30y+cPW+LD96Lt7pOL3kUHz+4gkndHn2zmn5t+EQfMbcl/Tf84fOXKnK/v8fee4suqA7/fYVv89NnjAJygPr/cJ7ZLUPOK/i5SKJeUvRmXQJ+JXtm91ZZNnbnFl+y4K0vQljW0P0smDfay53b/XtSDNu9oK0nuD/TNOEvfLWS1IWu7weQkFr6Uy4utsBTcrOLYr2DZH/ecq+v+4OejLBvf5Nhod7ptx4KHnurJ1t/t+b/rU7a7M+v2+3Az+n7aq/6wV/T8VQDGnxOC27TclbZP0EmUD2fcHy7xL0uuWs1MAAAAATm7cCqg3nCqnJSul1EopvVTSM5Vdg3uvpMn2v1dLekZK6WUpRTfTAwAAAACczHp+5jal9HpJr5/H8h+T9LGl6g8AAACAVWYpAqCYuS3dKTNzCwAAAABYvXp+5rZXNWVqqqKmosCmhYdkrK2Ou7Jx80EH1SCEabzll4tColodjjJVgjqrBUMoTjR9YMS+cZ/60AiCM+744pl+uRHf7rrv+HV33+QDOyTpxE6/bP/xICjqG3tdWfO2O11Zy4LjSK1i73MUMrRYRQMslqJtzE+qB+FhQZlu9SEnRQ0GAWN9Oze7svH1cRJL36j/vDWGgu+2E0GQ3Rq/3OT6Ykeyw6+ioKzZ9J+/vr7481cLylvBa2nVfTBM/6APdjpzjQ9We/DwA65sd79PvtlcORH2cbZBiz/P26u+P8Pmv2v7gkCpYwXzg05b64Oe9u/Z4MoaQbbSiR1+v+t/yFlhO7X9x11ZutdvxzRe7Dsr+gzVdvv/S1rrfRDhwYf4JKT6cPH9uNkfbNyzR11RXxDMFAVFFQ2PihQOiurwkYzC2qK/BcJ2grJWlBK33r9XzbW+4UbDf84ntvr6akEYlRRvsqPnBMFVz/TBU/3H/Gve8BW/f0bRuI07vhv2ByefpUg3Ji25fMzcAgAAAAB6HjO3AAAAANBNUuGzIuZVJ0rF4BYAAAAAuuBWQL2B05IBAAAAAD2PmdsV0khV1VNVrRSEFQTJDaMtHwIy0CFEpIgoPOpYkPgx1gxCpprxbjNQ9f05Vh9wZfefWOfKDo8OubIoHCLd7ANtdn/UB42o4rdh9ZAP7LB6vA37Pn23X3+db7t5+Ei4vpMWHhIGLLXGPff6wqBsyzf851mSqqdtd2VpzIfbtXZudWX1Tf57x1r+PK0ju33bJ3YFoTKjPkCmGQTzVIbj4KEofCoFZZGBfv99cusR/5oftmaPbzf4v2BnbcyVzeeI9HDFb4vjyYdM7Wv476ebJne4shvHdrmym+/f5soq48V6Gfw3pCPn+v8LJKl/m3//7cJNrqw67t/r4Zt98F8UFNWs+v1pbKdf7viuILhok2+3vi0IfpNk1eA8xEn/XkX7nQX7slV9mFEUAFWt+fe5EtTXCgKqWkGYY9bHMFHKdycIj0pBndFyUR/rdf93SLQd0oDvS30w/v+4MuDLxyaC75NBv+NWgvdvfONpvo/BW7/tM8F3zgP7wz7O1jp2rNByJ50oiaxXTs/tlX6ewpi5BQAAAAD0PGZuAQAAAKALrrntDczcAgAAAAB6HjO3AAAAANANtwLqCQxuV1gUFFVPQTBBkLxRl1+ur+IDEfZN+iCkyZZ/66PwqJr5gIY9xze4Mkm6e89mV2bHfTvVsWKnYPSd8Mttv96HoVT++zZX1hr14VGLjXQqHB4FrFJpIg5hau314ScpCGur1oLvg0O+LA37kKmRoeD7bsyXHQ6+F6N8o/qa+L+/OPgm+M5qBEE1Dd/2YM1/Z9057r8rHzv8HVcW5C2pLwpi6WB/03/rHQv+L7lu7BxX9q3Rna7si/ef7cqiAK7I5PoguKg/CB7qkJNYDYJ9qtHuGGye/Q873ZX1H/H9Gd/sV66d8PUF/32qvj74H6bDW5X+f/buPM6Ssr73+PfXp/eefYFhlVWBiF4VCEQRjEuMaNyiUZOboBDN5nWLYmK8EpJrbtQk5oUmghtqFhMT3CVRI0ElICOXRJRl2EZnBmZjmLX3c373j6pO1/TvOUt3nzPT1fN5v1716jrPeeqpOlXndPdznqpvJd47Su3HRPBUKmTKx2JZ11Dckam3TqosFeAkT/8HPpkIQvOJ2GgqMK2rJxGElWCJjUwFa/lEiycj1qlW6YnHMFWmpTEobHR//F9ubHV8z/btjGU/fkUMnhrYEQPdjrplVyjr3pt4g0qqPrI1FlrihSfCv5K/55MBUIn3RCLETh6Pc2V5DBatdA9IO+LiwGzRuQUAAACAhkx1v7WaV5toJ665BQAAAACUHiO3AAAAANAI19yWAp1bAAAAAGiEzm0p0Lk9TO7Zf4yG9g2pKxFesm+yL5R97+4Y+LFkdQxNGhuLh7S2aSiU9Z68Ly47GoNGujbHYJdE3pUkadmWeN1AIrtEyx+KAQ39j8bQlcqN/y+9opnraKkWgE5JBbilTG55eM7r6Lsrlg2uXRvKlv8whjVtuCyG4I0+Fn+3SVL3kvi7yCrxt4wnwqNGD8RQmd618ffdSCJIsJL4DycVgpf69TteJ+xna3UwlO2oxiCXB0aPCmUbD8T9ODoR/75U98Vf8pVEKFTqqrJab2K7467J1jOQaDPxy7/VW0ZODKX+XiWOQeJtkvq7ZokV1zks6QvCUiFTqbIW1RIBXKmsrkp3fJels4PS2+LVRHkqgK3VZVOLJsKjLBW2FT+6UuJ/rNTnuZ5UuFYltfySGDJlSxOhZUsTn/1d8Q1V64tvkkeeFT+TAztWxm2R1P9YDKQauD8G/2k8sdMSQXRei6958tQYhOWVuN37Hhf/p+3fFdfxyEkHpA/HzQFmi84tAAAAADTiav0btNm0ibYiUAoAAAAAUHqM3AIAAABAA+4NLjeYR5toL0ZuAQAAAAClx8jtYXLzhtPUvXN5MjCisjcelrU/jG0Mr4shKQNjsV7/rvi10PLrY9hEdTB+17H71Nje6jvT4TE9P9kRC3tjUMLkQz9OLg8As1HdEX/ndB0XQ6aO/l5cdueT03/+JlIhPn2JAJlE0ExXdyzbMRwD/S5cfV8o25oIejq6EgNgqomv+evF46TCo+4aOS6UTSRSArcfWBLKhvfHYJjKvkTEVeKaNEuF/SS+Xvc6X7mnLnNLhhsmAoQ8cai9O+41Sxx7H41lkysSUV+J9Xb1piLB0uFMnnrftRjMlJR4f3qrQU+JkSRLvD4pHeykVFlKYnusK3FcWtwN1ttaUFS915IKj0qVpbZnsD8GStUSxznV3mhif42uisuOL4//Tw0fk945leEYXDV5cQyAOvY7MWbManF7xpfFD9v4kvhhTWTlafcTEr+z+hOBbttLEA9KWnIpMHILAAAAACg9Rm4BAAAAoBG3DqQlt7k90LkFAAAAgEZMySsQ5t0m2ovTkgEAAAAApcfI7WGy5pZu9Q71JMMzVn7mtlBWWRJDSap798Z6p58Symw0EXTw6K5Q1lWN4RdH3RJDBCwREiVJk7v3JMsB4FCxLTFkankqhKl7eXL5XT8Vf+dNDiUCgPpj+ElPbwxnGZ+M7W0aXRXKTu/bFsr21OKygxZ/Tw8nk5WkQYsJg/1dMdlppBp/p1driT9OiSGGrvFEKNA8cmHqLZsKhar1JkKTehKBRK0GKbUYWmXVxPshFTpWRyrQKDkYlBoiSm1kKhwrEVJkLYYjpbavKxH0JEmVxLonJuJO89Q+S+3vZNhWYhsTr68rtR/iouruSQd9dXfH8p5Kum5YttLa8e9OtDeUCKManYhv+LGB+NmdGE//G1/dEZOdqgNxG7efEz/7tbP3hbLxrYNxJcvidvcvib9zuqvxd0nqPTahkbiOhYZAqVJg5BYAAAAAUHqM3AIAAABAIwRKlQIjtwAAAACA0mPkFgAAAAAa4ZrbUqBze5gs++fb1W+D6TSHRPhJKjwqpXrfg/PdtIM3JVU43NZVAEDbVHfEQKnu/r5QtuLu9IlL/Y/G4JRtPx1DV0aPjr8dx8fin9TenpiGs3tiIJT9cOT4UHZCdwz+S+mq89/RsMfX3ZcIlOrritu4bzguW9sf90MlsRtb/l9tNsumnkiECiXbTAQppdpL/Tmu9iaCghJllggzSoUtSVJ1ch4nzSUCs7r6YkiRJ0K0Wi2rJpIuexNhaVLy35WkWuo1txj0pVQQViowK7FoJRESVakT/tQ1j3u8pIKwUuFRvYntHuqJwUyKvyLU1x2Pwd6x/uT22DGJwK3E6xs9Jf7OSoXJ1U4dDWVL+2J41NhkbG8kEY5VSRzTfXvT7zHMj5kdJem8fDo3n1bnT3/K3S9t03qulPSeFqs/y93/vR3rTaFzCwAAAACNlHPkNkbxL3J0bgEAAABgcdsk6W5Jz+vwes5u8vxDnVw5nVsAAAAAaKScI7dXSVovab27bzOzk9ThzqW7/7CT7TdD5xYAAAAAGinhrYDcvdXrYBcNOreHW6tpDACAOZnctDmU2SNbk3UHTz85lK0eXBXKHkuERw2fkghxWdra7/gJr4SyrdVloeyk7sdaak+Sjk3U7VIM07lp9PGhbNlQDJDZsTuG10ysSoTApIKCEhk+Vo31usbT/+gldk86KKrVUKBUlmMqoCqVWZUIcLLEtnT3xHqSVK3GwB5LhFQNLI3HYLAvBoLtH4nhX2MjMfzLJ+J6J8YTQU+J8KeRSmxPkiwRcOWTnf/nPxWEVUmEXqXCo+r925UKUkpJBTONtxgS1pUIhZpMBHj1dMX3Tio47qjBfcn1TLb4Wlb2tfZ5mazFD+BoNf4O7OpJfA660gFeM/UMkVaK9qBzCwAAAAANmLf+/dls2kR7zSOPHgAAAACAjJl9w8weNbNxM9tuZv9uZu80s5WHYv2M3AIAAABAI50PlFpnqRtuF6u7x+tsFp7nFObXSroon64ws0vd/YudXDmdWwAAAAA4vNa3UKezCVTzc6ekL0i6TdLDknokPUHSLyu7/dAKSf9sZi9y9xs6tRF0bgEARxyfTAQhSarefV8oW7Y9BkpZ7fRQNrAz/kl99Lyloey7e04LZT2Pj6ErJ/fFQKnVXQdC2WBXDBmqZ1lXDCla3hPLVvaPxIWPi0W79w6GsmoiXKcrEdY0uac3LjuQDmFKhRx1DcXXXUvUSw2EeCLMKhWO1D84HsqG+mNZJRGaU6uTgrpqSQzOSQUALe+Lx6WnEvfPsv5Y7xGL753JxL6pTsagoNpIYt8kQrSyyonXmHrZqfCv1P5JXICYHMhKNJcKj+pKrNdnkU47n0G60Yn4+6Ca2F+pIKv+RPBUb+LY13uPpaSCsFpdvpY4qNVEEFaqvYnE66sktmU2xwUL0gfd/cpE+fckfdrM3iDpI5Iqkj5mZqe5e+IPzfzRuQUAAACAw+tcSeko/wXO3Xc3ef4aMztH0uWSjpX0Mkl/24ltoXMLAAAAAA2YOpCWfPDDrSW5pnaurlHWuZWya3Dp3AIAAADAIeeWPp1+vm0eOe4qzCcudGkPbgUEAAAAAOikQ9KTZ+QWAAAAABrp/K2AFruzCvMPd2oldG4BAGig+uiuULbkhv8KZUtPfVwos8mYtLzz/LiOe3cfFcpW9cZk5NN7Y9ZIvcTTfovpqrs9puM+benGUDZS7QllRw/uDWU/6Ymvb99YTEGeqMb1Vvti2nG1mj6hrLs7JuGOj8c2u3vja55M1BtYGhOPuxNpuwO9cRtTSbZ9lVi2sj+mIkvS/om+UHZ0/75Qljr+E4njN1mLZT1diVTl3piqvGX/8lCWSu8dSyT/StLoeHyfdHfHdY+MxPfEvCTe86n3TnfiuFQSqcNSOq039b71xEWXk4nU6VbXkSpLvcf2jcf3TZel92vq/ZhKSz4w0d7jMjqZSohOfKYTx8DafTErFqI3FOZv6tRKOC0ZAAAAABrxDk0LnJldamaeT1cmnj/bzOI97g6u8wZJl+UPt0r6fPu3NMPILQAAAAAsMmb2DEnFjueawvxpZnZpsb67XzeH1TxN2b1rb5R0g6Q7JT2qrJ95hqRfkfTcvG5V0hvcPZ6a0iZ0bgEAAACgAfMO3Aqo8yO3l0v6tTrPPT2fiq6b43oqkp6TT/U8Kukyd//SHNfREjq3AAAAAIC5+JqyU44vkPQUSUdLWq0sHXmXpP+S9C+SrnP3GN7QZnRuAQCYpdpoDOfpeuDHoWztlhgA1TV5Vih7ZCQGSv14yZ5Q9r3eeFnTUwc2JrdxVSUGGq1IlJ3dvymU7VqyJJTtr8ZAm75EcNGW4RhStHesP5RVU6FAqfAZSdVarLt8cCSU9XTFUKhUUE1vYrvHqvFfokqivaU9Y6Gsy2K9ekFfxw3uDmUreuJr6e+KYVYTqfCoRIDTwIoYmJUKo1reE9/HY4l1VOoML23cFwPFUgFJtiSegTieCGvauSe+73p7YzjSkv54DFKOGYr/R28fXpqsm3o/Ku5GjbccHhXLaon3sXXHiqlgptR7sd57bHgiBn2ljku95VtZd0q9z28r6+0uQ6BUCdOS3f1SSZfOs43r1GBE1923S/pEPh12dG4BAAAAoJESdm6PRKQlAwAAAABKj5FbAAAAAGigpIFSRxxGbgEAAAAApcfILQAAbZAKmbJEqszq2x8NZUs3xyCdjafHsJ5jB2LI1PG9sT1JqiqGtqzoisFFo4nvuZ88GMOx7htbF8q6EsMOx/bHwKT79sfArPFEcNF4Lf1vSbfF0KSUwe4YwjSaCIrqTgTk9HbF4KLU9qTqzUbN4/7ePTEQynosBnh1J4KwlieO6fLuWDbYFdORRnti8NBwrTeUjdU5LkOJ4KrUaxmejOuZTAVc9SUCrhLHryexH5b1xmVPG9oRys5e/nAok6SfjMTP2+YDK0LZ1r0xkKra2tszGSg1Oh73TSoEbThVr856xicS+6wnvm+r1RYDoBJBUQN98dhPJuqlgqyGEsv2tPgZP6zcsqndbaKtGLkFAAAAAJQeI7cAAAAA0AzXyC54jNwCAAAAAEqPkVsAAAAAaIC05HKgcwsAQIf4RAxxqT0Qw5qqJz4plO3YviyUPbhkTShb07M/ue413fviehInbJ3euzWUndAdQ6FSZZsmY+BOKnjqhMHHQll/Vwx/2j8ZQ5Qkacd4DNzqsRgK1VeJ+3tNXyyrKC67vxrXXUn859mVOC9xIhESlVq2nlTIVNw7Urdi6E41sWxKKmCsktiHqeCp1LGSpL5EuFZfou5EbwyPSoVUbRuJ7/neRHjUkp6xUHbiwK5YrxLrrepOf14emxgMZangqt7u+JpToUljozEAKlUv1buZnEwc08SykxNxv0pSV3c8rvXqhs1JvW8TZQdG4uclkZ+nSiUWVnvi6xutxv0FzAWdWwAAAABoxNX+a24ZuW07OrcAAAAA0EgHTkumc9t+BEoBAAAAAEqPkVsAAAAAaITTkkuBzi0AAJ1Si4E0PhbL+r55Ryg7679ieNRdbz05lG05I4bwSNILT/xRKNszORDKxmoxyGVjZTSUndq7LZStq+wNZWsHY5DVvv7+ULZpYnUo29a1PJRJ0rq+PaEsFcK0a3IolPVY3N+pIKVUONKExxCeA4ngqVoiDCcVPCWlQ4rq1Q3rSYQKpcp6EudOTiQCnGqJkKlWA6qk9P7p7or7tiuRNJQ6LuN9w3EdtbiOrsTxS21LSipUTZKO6o3v265lcbu37Y/hZgO9MURrfCyxv2uJQKnkMY2vpTYZ63mqPUm1xGtMBkWlWGwzUZRszxLHvlW9iTA4YC7o3AIAAABAI4zclgLX3AIAAAAASo+RWwAAAABowDqQltz29GUwcgsAAAAAKD9GbgEAOMy8mgieGhkJZY+7YTyU7b07BjNJ0heWXRTK9j01BkU98XEPh7IVvTHY54H+o0LZs5fG0KoJxTCc1V0HQtkpvdvjeitxvZK0uzrYUlkqpCglFZq0PxUUlai3ezyGcg1UYqBQX52AnFR4VHciZCql1deXCldKvb6UVEBVPalwpopaCxXqSgxZreqJ75NWg6JS250KDksdU0la0x0DpVZ2x+2pHBvb/OHuY0PZsv74Wdu5PwaeTUzG1zc+FgPKrBL3V1dPel+ngqZSoVCV7vh+qlVbG/dKDTh2J7axO7GO1L4ZGB9rab1AM3RuAQAAAKARAqVKgdOSAQAAAAClx8gtAAAAADRAoFQ5MHILAAAAACg9Rm4BADjcPH59X929J5T1fu+eULZ22/HJJveeuSKUja3sD2U/fOykuDkDMQTmcSftCGXf3npqKHv2MRtCWSrYJxWO9NDwmlAmpcOZVnSnw6dm2ja2rKV6+yZj4FJ/Iijq0dEYCjTQHev1J8okaWl3DM6ZSIQcLanEemO1+G9bLZEU9Mjo8lCWCq1KhStNpsK2JtJhVL1d8bikgqJW9sZwtJ7E9lQToVCpsK7UeycVPLVzYkncljrvm6WVGHK0vBIDpS5cFt/fL1jxX6HsvrF1oeyHB+JntSvx2fjBruNC2fBEDJkaHusNZZJUSwRKdXXF49Luel1d8bUs6Y/v41T41/krHtQNoXQBYqR1wWPkFgAAAABQeozcAgAAAEAjpCWXQulGbs1sjZm9w8xuNrOtZjZmZg+b2ffM7P1mdkELbVxgZp8xs41mNmpmj5jZv5jZqw7FawAAAAAAtFepRm7N7BWS/lrSzDvWH5NP50k6XdJLGrTxvyW9Rwd37Nfl08+Z2WskvdLd48UXAAAAAI44pCWXQ2k6t2b2q5I+qaxTul1ZJ/e7knYp65ieKulFktIJDlkbl0v6w/zhA5LeK+lOScdKepOkZ+VtfEzSr3TidQAAMFe1AzHgpmvj5mTd5ftjcE7frqNCWbUvnsS178T478HmfTEgp9Yf/zP7+3vXxm0ciwEyiRwdTayOAUWSZL2xcnd//HNfq8XX4tW47uOO2h3KtuyIAVx9iXVMTsTgoqHBGJpz9NJ9oUySuhMvfLQa9/cjtRgKlbJrZDCULemN21NNhUcl9lcqHGt4Ih1cJMWgqZ5KDHsaTwRhtRpGNZIIlKok6g1VxkPZpOKxSoVRSdLyFgPKju1+LJT1J9pcPfBgKDuh99FQ9sORE0LZyPK4v+/fG8PWehP7WpJGJuL+nqzGfVFJnL+ZCnuyxP5OfdZ6u+Mx7U+UPfvoe0PZsbvjfgXmohSdWzM7U9K1yjq2SVEZtAAAIABJREFU35H0InePMZLS1WaW/A1sZiskvT9/+BNJ57v7zsLzX5H0eWWd2182s2vd/dttfBkAAAAAyohrbkuhLNfcXq3s68Gdkl5Wp2MrSXL3+NVd5tclTX0te0WxY5svV5X0W5KmvgZ7+7y2GAAAAMCiMHVacrsntNeC79ya2RmSnp0//NDMTuksTF2Hu1fS9akK7r5Z0jfzh881s3iDNAAAAADAgrPgO7eSXlGY/9zUjJmtNLPTzWxmuFSQn6p8Xv7wlgaju5J0U/6zT9K5s91YAAAAAIuMd2hCW5Xhmtvz8597JN1tZr8s6R2SnjRVwcwekvQpSX/m7vsTbZyu6dd6T5P1FZ8/U9KNc9loAAAOhVTIlCTVhmNATvePN4Wy3qVLQ9nQqhiutOK+GEbVvzEG5Eysi0FIm54zFNu7PwYrDa/tCWWSVBmL/wF2VWPExu4nJOrVYkDO9gePCWW9iWye3t0DoWziuES4ztb4+u59wrLYoKQNk3F7tCyGJqWCsHoGYr2enrjhWx5e1dKyXZV4DGqJ/dWfCNaSJE+EDyVDhXricU2FQo1MxHp9ifaGehLhUYnArOW9I6FsZyUeK0naX43hWD+7/O5QVkusZyLRQ0mFTK2txJCxx/W2dkJiXyKA6yfDK5N191XiaxlPBEqNjCc+b8mgqPhaqrX43hnoje+TJ6/aEsqO790Vyo7riSFvwFyUoXN7Vv5zo7Jrb387UedkSVdK+kUz+zl3f3jG88UounSs5LTiX/4YYdeAmR3fpEqMmgQAAACwsBEoVQpl6NxOff14hqQnS9ot6Z3KrpvdK+lsSVdJ+nlJT5T0OTO70N2LXykVv5ZOjewWFb8Cn+01t/ErcQAAAABAx5Xhmtup80f6lCUZ/7y7X+PuO9x9zN2/L+mFkm7I6/2MpJfNaKO/MN/oeltJKt4cLp6PBAAAAOCIQlpyOZRh5HZU0x3cz7n7rTMruHvNzN6ubPRWkl4t6Z9mtDGl3p3IpxQvVIgXazTW7DTmdZLWz7JNAAAAAEATZejc7tN05/aGepXc/UdmtkXScYopx8Ur+JudalxMGmh2CvPMbWh4Pa9ZIkQCAIBO8NaGBGr7YshNqqx/LBHis3VbKLMH4zpO/I+WNkUr+vuT5V1r4o0R/EAMzFqzP4ZrdZ1yYmywO4brTK6IJ2vVeuIJbuu+MxbKKo/F/TV+QvpmDmOrY4jPnpPj655MnDs2sSR+Pz+ZCMLqWhqP/cRY4mS9SuI90pVY9kCdcYFUOFZ3XN5G4/72nhhI1LMs7ttaIuBo6dBoKFs+EMu2DcewtJX96XGL0cl4XO7ti8FjSrxF11b2hrJ9tVhx1OM6Brviax7sip+1tb3xPdbXlQ76emD/2lA2knh9gz3p5WeaSIRRDSZCvXq74pvxpP4YmHV2X/x3eVmlBCeTcs1tKZTgnXTQdaythkHNjHQsLtcs9Kk4+so1tAAAAAC4DVAJlKFz+6PCfPzq6GBTz8/MS9+g7HpdKQumaqT4fMyABwAAAAAsOGXo3H67MH9qk7qn5D8PuqmWu49Lui1/eIGZNbru9qL855ik77e6kQAAAAAWJwKlyqEMndsvSZq6KGBmCvJ/M7OLJE1d4PKdRJUv5D+X1Wsnv0/tc/KH/+bu8QIHAAAAAMCCs+A7t+7+qKSP5Q+fa2avmlnHzJZK+mCh6JpEUx+TtCef/79mdlDSg5lVJP2Vpk9t/sB8thsAAADAItHu62257rYjypCWLEnvkXSJpBMlfcbMni7pekl7JZ0t6QpNXyv71+4ebrfj7rvM7ApJH5H0OEnfM7P/I+lOScdKerOkZ+XV/97db+zg6wEAoFRSycjtVhuNibeSVNu8JRZ2JWI4ajGttXrv/S2tO3U/g1TQR+p/0drgYCjr+klimyUtecIpseyumBxs+2Ly8yMvPjmUja2KW949HMu8O45n1Hriq6nFUF1VB9L/gXeNx/Ukwn+TbdZ647KTI3E/+mA8prtH47+vuydjMnJlSUwDHlmS2BhJg72x7q7JoVB2y/7TQtkxvbtD2bE9jyXXM9NoYuekUpCXVuJnY8d4fM2StLIvJol3WSK9uRrXXfN4XHp64jFIJSOfvnR7KLtwcEMo67e47HdGl0l6OJQDs1WKzq277zCz5ys7Rfk0Sb+TTzN9QtKbGrRzjZkdK+ndyq7f/USi2tckvW7eGw0AAABgUejENbJcc9t+C/605Cnufrek/yHp7ZK+J2mXpHFlt/n5B0k/6+6XuXvDm3a5+3skPUPS3ym71c+4pO2SviHpNe5+ibunvzoGAAAAACxIpRi5neLuB5RdCzuv62Hd/T8ktXhLeQAAAABHtE5cI8vIbduVZuQWAAAAAIB6SjVyCwAAICkZHnW41IZjgE891bvva6me9fSGsnV/F9ez5/lnxXWkMpMSiVnVRKjTxFCibEkqbivdZmokyhPJXN6VCJQajAvbSPxXtZZK+uqKy/qBWHH3RHpcZ3RoPJR9a/LxoWzt0P5QNrkstrlpdFUo6+6KwWFreuJdJ/ttMpT1JEKYVvfGbZEkjS+J6+6L6x6oxNe8fSyGVA1UEgFX3fEKvpXd8f35wMTaUDZc6wtl3923VtI9oXxBYeS2FOjcAgAAAEADpvT3OfNtE+3FackAAAAAgNJj5BYAAAAAGuG05FJg5BYAAAAAUHqM3AIAACwwPhHDfrRqRSha+tCBUFbZsScuW42BQj7YH8pGTl4ZyvaclP53sToQrxispaomsr9SoVc9+2J71f44tNWd2DW17rhsMshqZwzqkqTJvhjiNDoRX8zmPctD2c7hoVC2sn8klA10x2CmySVxnGlJ91goe2xiMJSt7onHXpLW9cXjX/W4nrHEwVo2FIOiKhbfO4Nd8SDsq8b30w+GT4xle44LZXc+OBDKFhyXjJHbBY+RWwAAAABA6dG5BQAAAIBGvENTB5nZUWb2QjO7ysxuMLOdZub5dF2H1vkqM/tXM3vEzEbNbKOZfcbMzu/E+mbitGQAAAAAWHy2HaoVmVm/pM9JeuGMpx6XT68xsyvd/Y86uR2M3AIAAABAMyUatU3YJOnrHWz/45ru2N4o6SWSzpN0maQHlPU7rzKzyzu4DYzcAgAAlEF12/ZYmCir9vWFMh+LIUVdgzGkqG8wLrt2V3osZPcTloSyiZitpL498b/4rkTIlNViveE1cd3jKxLhUYlN7E3kak0mQrAkaWJ/fC0HVsaN9L4YrlRdMxzKdu2JO2LV8hgAtW04rnf1QGwv5Se2Kll+4tCuUDZQiWFW+yZjANT+yRi4VUmkKA1VYqDU1tGloWzL/hjAtXXj6lDWvWF/KFtorAOBUm0PqIqukrRe0np332ZmJ0l6qN0rMbOLJL0mf/hlSS9196kP0Hoz+5Kk2yWdKOl9ZvZP7r673dshMXILAAAAAIuOu7/H3b/i7p0+Pfkd+c+qpN8qdGyntmOnpCvyhyuVjeZ2BJ1bAAAAAGikhIFSh4KZLZH07PzhN9x9c52q10vam8+/rFPbQ+cWAAAAADAX50maup7hpnqV3H1c0q1Ty5hZ4m7X88c1twAAAADQwCG45nadWfqa8CkNRkUPpzML8/c0qXuPpOcp64OeLumudm8MnVsAAIBFJBUelVIbjsFFXffGrBl/4qnJ5Ye2xZCiam88KXBwcwwL8u5Yr2vvSCibPG9tKOvdH3sYk/2NOwXTK07X6zkQ2xzYVgllw8fE7a5uWxbLjophVDt2x7Cmvp1xHfv3xm3cf+pkKFNPDLeSpLuWHB2r9sTtGX40BoqpEvdD90Bc9+Te+Fq6d8fXcvT6uI3HVhKBYNsm9GDcmiPN+hbqtPhGP6ROKMw363xvmrEcnVsAAAAAOKQ6cY3sIrjmVlIxJrtZ7HUxMjxGhbcBnVsAAAAAOLzOlbT1cG/EHBTvKRXvEXWw4mklAx3YFjq3AAAAANDIIbjmdusCvaa2mdHCfDxf/WDFG2nH6xDagLRkAAAAAMBc7CvMNzvVeKgw3+wU5jlh5BYAAACS0iFTuu3OZN2BdTG4yKvpkKOwnkd3hbIYeSSt2rojlI2dc3ooG18R/6Vddkc8w9OH6wwWrYihUOqOAUkjJyTCoxJhVpbIf+rfHte964mxL7D6zn2hbPSu/lDWvy39WiZWxLqbf3YolD3+ywdC2cSyROjVLQ+EstpPnRzKvCvuh+49o6Gsdv/GUDZ+TGL/LzRcc1tPcbT5eEnfb1C3GD61qW6teaBzCwAAAACN0Lmtp5h4fEaTulPPT0q6vxMbw2nJAAAAAIC5WK/pIKmL6lUys15J508t4+7NwqfmhM4tAAAAADQwFSjV7qns3H2fpH/LHz7HzI6vU/VlkqbOP/98p7aHzi0AAAAAIDCzS83M8+nKOtU+kP/slvRhMzvognUzWyPpT/OHuyV9rCMbK665BQAAwBxMbt8ZCz0GSlklBjMl1WKkVHXv3lDW/a3bQ1lPX18omxxPnPXodYbKtm1vvn2S+jbEwKXKsYlgrd6eUFbbGO/ysuq2xDZ2x3/P+yZjQpX1x+AoSeobGgxlJ98U96Mn2oxbLSUjwm79QdyeRLVUSFiK1Qv6WmhKNtJqZs+QdFqhaE1h/jQzu7RY392vm8t63P1bZvZZSa+S9AuSvmFmH5T0sKSzJb1L0ol59Xe6+2NzWU8r6NwCAAAAwOJzuaRfq/Pc0/Op6Lp5rOt1yk47foGkZ+VTUU3SH7n7NfNYR1N0bgEAAACgAXOX1Rv5n0ebi4W7j0i6xMxeI+lSSU+WtELSNknfkfQhd7+l09tB5xYAAAAAFhl3v1RZR3M+bVynWYzouvvfSfq7+axzPujcAgAAAEAj3Oe2FOjcAgAAYPYSAVApqeCidvOxsY6vQ5J8IgZATf54U3vX0eL+qo2Opp+oV76A1fbuO9yb0FQnbt2zGG4FtNBwKyAAAAAAQOkxcgsAAAAAjXBacikwcgsAAAAAKD1GbgEAAACgAa65LQdGbgEAAAAcNrWRkcO9CVgkGLkFAAAAgEa45rYUGLkFAAAAAJQeI7cAAAAA0ADX3JYDnVsAAAAAaITTkkuB05IBAAAAAKXHyC0AAAAANMFpxAsfI7cAAAAAgNJj5BYAAAAAGnHPpna3ibZi5BYAAAAAUHqM3AIAAABAA9wKqBwYuQUAAAAAlB4jtwAAAADQCPe5LQVGbgEAAAAApcfILQAAAAA0YLVsanebaC86twAAAADQCKcllwKnJQMAAAAASo+RWwAAAABogFsBlQMjtwAAAACA0mPkFgAAAAAacc+mdreJtmLkFgAAAABQeozcAgAAAEADXHNbDozcAgAAAABKj5FbAAAAAGiGkdYFj84tAAAAADTAacnlwGnJAAAAAIDSY+QWAAAAABrhVkClwMgtAAAAAKD0GLkFAAAAgAa45rYcGLkFAAAAAJQeI7cAAAAA0Iir/bcCYuS27Ri5BQAAAACUHiO3AAAAANAA19yWAyO3AAAAAIDSY+QWAAAAABqpeTa1u020FZ1bAAAAAGiEQKlS4LRkAAAAAEDpMXILAAAAAA2YOhAo1d7mIEZuAQAAAACLACO3AAAAANCIS/I2D91yzW3bMXILAAAAACg9Rm4BAAAAoBFv/zW3jNy2HyO3AAAAAIDSY+QWAAAAABrhPrelUKqRWzPrNbPLzOxfzOwRMxszs/1mdq+ZfcLMzm+xneeb2fVmtjlvY3P++Pmdfg0AAAAAysXcOzKhvUozcmtmJ0j6qqSzZzzVK+nx+fRaM/sLSW9zj+8WMzNJH5H0+hlPHSfppZJeambXSvqN1PIAAAAAgIWpFCO3Ztatgzu2P5B0qaQLJD1P0lWSDuTPvUXS79Zp6o813bG9Q9KrJZ2X/7wjL3+9pD9q39YDAAAAKLVahya0VVlGbl+s6Y7tLZIudPdq4flvmNmX8ud6JP2emf2Fu09OVTCz0yS9I3/4fUnPdPeR/PH6fPmbJJ0j6Qoz+6S7P9C5lwQAAAAAaJdSjNxKenph/k9mdGwlSe5+u6Sv5A9XSjpjRpW3aLoz/8ZCx3Zq+WFJb8wfdkt683w3GgAAAED5cc1tOZSlc9tbmH+wQb3iSGvf1Ex+re2L84f3uPutqYXz8nvzhy/JlwMAAAAALHBl6dxuKMyf0qDeqflPl3RfofxkZaFRUnbqcSNTzx8v6aQWtw8AAADAYuUdmg4RMzvRzD5gZneb2QEz22Vmt5nZ75rZ4DzbvtTMvMXp0ja9pKSydG7/XtLefP4KM6vMrGBmT5F0Sf7ws+6+t/D0mYX5e5qsq/j8mXVrAQAAAMACZ2aXKAvkfZuySzcHlV3Gea6k90v6f2bWaACxNEoRKOXuO/Je/t8qu/52vZl9UNmI7pK87G3KTl/+T0lvndHECYX5zU1Wt6nOck2Z2fFNqqybTXsAAAAAFgKX2n6NbOeHbs3syZL+UVmHdr+kP5F0o6QBSa+S9OuSniDpq2Z2rrvvn+cqf07Sww2eb9YXm5dSdG4lyd0/b2bnKOu4vk7Sp2ZU2SbpPZKudfcDM55bWphvdsCKyy6Z5WZual4FAAAAAA6JDyrr2E5Kep6731J47ltmdp+k9ykb0X2rsluszscGd984zzbmrCynJcvMeiS9RtKLJKWCno5Wdr/aixPP9Rfmx5usaqwwPzCLTQQAAACwCJl3ZuroNpudq+m+0cdndGyn/Jmku/P5N+d9rtIqRefWzIYkfVPSuyStVvbtwpnKEpGXS3qepO8qO2/8y2b2phlNjBbme9VYX2F+pG6ttBOaTOfOsj0AAAAAh5t7Z6bOeklh/pPpl+U1SZ/OH65UeqCwNMpyWvIfSnpmPn+ZuxdPSR6X9A0zu1HS1yU9S9Kfm9mN7v6DvM6+Qv1mpxoPFeZndc65uzc8h5w7CwEAAAA4RC7Mfx6QdHuDesW7yTxD0jc6tkUdtuBHbvN7zb42f7hhRsf2v7n7pKR35w+7CstIB1+43Cz0qRgixTW0AAAAwBHOap2ZOmzqzi/3532letp5t5jrzGybmY2b2U4zu9XM/tjMjmu+6PyVYeT2aEmr8vk7mtQtfiNxRmH+rjrlKcXn765bCwAAAADaY12zszybnSVaZGb9ktbkDxsu5+6PmdkBZWewzupuMQkXFeZX59NPS3qbmb3Z3a+ZZ/sNlaFzW/yWodn2Fi+ALi73kLJI6mN18A5PmTr9eYukjS1sHwAAAIDFrBPXyB7c3voWlpjNNY6zuVuMlJ26PKTZ3y1myoOSrpd0i6bPfj1F0ssl/aKygN+PmJm7+7VzXEdTC/60ZEm7JO3N5y8ws0Yd3GLH9aGpGXd3SV/MH55hZuenFs7Lp0Zuv5gvBwAAAABlMpu7xUjTd4yZy91iPi/pNHd/u7tf7+7r8+kf3P2Vkn5B0kRe9y/MbN0c1tGSBd+5zRO8vpo/PFZZYnJgZisl/Wmh6CszqnxQ06O5V5vZQQcuf3x1/nAyrw8AAADgSOcdmqadq+Z3XpmN2dwtRpq+Y8xs7xYjd9/TaFDQ3b+iLCBYyu65e9ls19GqBd+5zV0laTifv9LMvmRmLzezp5jZBWb2Fkn/KemsvM6/ufvXiw24+wZJH8gfniPpZjP7JTM7x8x+SdLNebkkvd/d7+voKwIAAACAzFZ339xommV7s7lbjDR9x5hZ3S1mFj6q6e58s8tE56wM19zK3e8xsxdL+ntlF0a/KJ9SviXpFXWee5ekoyS9TtJTJH02Uefjkv5gXhsMAAAAYNEwd1mbr1hsd3tF7j5qZjuV9Z0a3i0mPwN2qnPbkbvFuPv2fHvWSupYcnJZRm7l7t9Udj3sFZL+XdIOZedujyi7vvYfld2o+Dnu/lidNmrufpmkS5Rdg/uwsnPQH84fv8DdL89PhQYAAACA6UCpdk+dNXXnl9Oa5BYdqrvFzCYQa05KMXI7xd0flfS+fJpPO1+T9LW2bBQAAAAALDzflXShslHZp0n6Xp16xdOEb+7EhpjZUcpuCyRlA4sdUZqRWwAAAAA4LFxSrc1T5+/L8oXC/GtTFcysS9Kv5g93S7qxQ9vyek2P3N7UoXXQuQUAAACAxcbdb5P0nfzhZWZ2QaLa2ySdmc//pbtPFJ80s4vNzPPpupkLm9lJZvaURtthZi+U9O784aikT87iZcxKqU5LBgAAAIBDrWyBUgVvUnaq8YCkr5vZe5WNzg5IepWyEVVJ2iDpz+bQ/kmSbjSzWyR9WdkdbLYrG6U9RdIv5tPUqO3vuvuWOb2SFtC5BQAAAIBFyN3vyG97+jeSlkl6b6LaBkmXuPu+xHOtuiCf6hmW9BZ3v3Ye62iKzi0AAAAANOJqf7rxIRm4ldz9y2b2JGWjuJcouzXQuKT7JX1O0ofcfXiOzd8u6VeUdWzPkXSMstsPdUt6TNKPJP2bpI+5+/b5vI5W0LkFAAAAgEXM3X8s6a35NJvl/l0NbuGTj/b+bT4ddnRuAQAAAKCRTtyX9tBcc3tEIS0ZAAAAAFB6jNwCAAAAQCNT96Ztd5toKzq3AAAAANBAiW8FdEThtGQAAAAAQOkxcgsAAAAAjRAoVQqM3AIAAAAASo+RWwAAAABoqAMjt2Lktt0YuQUAAAAAlB4jtwAAAADQCNfclgIjtwAAAACA0mPkFgAAAAAaqeVTu9tEW9G5BQAAAIAGzF3W5tOI290eOC0ZAAAAALAIMHILAAAAAI0QKFUKjNwCAAAAAEqPkVsAAAAAaKTm2dTuNtFWjNwCAAAAAEqPkVsAAAAAaMTVgWtu29scGLkFAAAAACwCjNwCAAAAQEMdSEtm6LbtGLkFAAAAAJQeI7cAAAAA0Aj3uS0FOrcAAAAA0Ai3AioFTksGAAAAAJQeI7cAAAAA0IjXsqndbaKtGLkFAAAAAJQeI7cAAAAA0AiBUqXAyC0AAAAAoPQYuQUAAACARkhLLgVGbgEAAAAApcfILQAAAAA0wjW3pUDnFgAAAACaoTO64HFaMgAAAACg9Bi5BQAAAIBGOC25FBi5BQAAAACUHiO3AAAAANBIrSZZrf1toq0YuQUAAAAAlB4jtwAAAADQCNfclgIjtwAAAACA0mPkFgAAAAAaYeS2FOjcAgAAAEAjNZeszZ3RGp3bduO0ZAAAAABA6TFyCwAAAAANuNfk3t5b97S7PTByCwAAAABYBBi5BQAAAIBGXO2/RpZLbtuOkVsAAAAAQOkxcgsAAAAAjXAroFJg5BYAAAAAUHqM3AIAAABAI7WapDanG9dIS243Rm4BAAAAAKXHyC0AAAAANMI1t6VA5xYAAAAAGvBaTd7m05Kd05LbjtOSAQAAAAClx8gtAAAAADTCacmlwMgtAAAAAKD0GLkFAAAAgEbcpRojtwsdI7cAAAAAsIiZ2Ylm9gEzu9vMDpjZLjO7zcx+18wG27ie55vZ9Wa22czG8p/Xm9nz27WORhi5BQAAAIBG3KU2pyUfqpFbM7tE0t9KWl4oHpR0bj5dbmYvcPcH57EOk/QRSa+f8dRxkl4q6aVmdq2k33Dv3Atn5BYAAAAAFiEze7Kkf1TWsd0v6V2SfkbSsyV9NK/2BElfNbMl81jVH2u6Y3uHpFdLOi//eUde/npJfzSPdTTFyC0AAAAANOA1l1t7Bxw7OIBZ9EFlo7STkp7n7rcUnvuWmd0n6X2SzpD0VklXzXYFZnaapHfkD78v6ZnuPpI/Xm9mX5J0k6RzJF1hZp909wfm9GqaYOQWAAAAABrxWmemDjKzcyVdnD/8+IyO7ZQ/k3R3Pv9mM+uZw6reoulB0zcWOraSJHcflvTG/GG3pDfPYR0toXMLAAAAAIvPSwrzn0xVcPeapE/nD1dqujPckvxa2xfnD+9x91vrrOdWSfdObVe+XNvRuQUAAACABrzmHZk67ML85wFJtzeod1Nh/hmzXMfJykKjZrbTaD3HSzpplutpCdfcAgAAAEADYxqR2twXHdNBZ++uazaY6e6bZ7mKM/Of97v7ZIN69ySWme06ZrbTynoemuW6mqJzCwAAAAANrPd/6/gqWqjT8qm8ZtYvaU3+sGGn2N0fM7MDkoYkndDqOnLF+s0635vqLNc2nJYMAAAAAIvL0sL8/hbqH8h/zvZ2QLNZz4HC/HxuO1QXI7cAAAAAEG1Vh0YYZ1gnaYekahvb7C/Mj7dQfyz/OdDB9YwV5me7npbQuQUAAACAGfLrVGd7netcdGIdo4X53hbq9+U/RxrWmt96+grzs11PSzgtGQAAAAAWl32F+VZOAR7Kf7ZyCvNc1zNUmJ/telpC5xYAAAAAFhF3H5W0M394fKO6ZrZS0x3PTY3qJhRHnRuuRwef4j3b9bSEzi0AAAAALD535z9PM7NGl6OekVimVXfVaafd62kJnVsAAAAAWHy+m/8ckvS0BvUuKszfPMt1PCTp4UQ7Kc/Mf26RtHGW62kJnVsAAAAAWHy+UJh/baqCmXVJ+tX84W5JN85mBe7ukr6YPzzDzM6vs57zNT1y+8V8ubbraOfWzI4ysxea2VVmdoOZ7TQzz6fr5tDe883sejPbbGZj+c/rzez5s2ij28zeYGbfNrMdZjZiZveb2UfM7KzZbhMAAAAALDTufpuk7+QPLzOzCxLV3ibpzHz+L919ovikmV3cQv/tg5Im8/mrzeyg2/zkj6/OH07m9Tui07cC2taORszMJH1E0utnPHWcpJdKeqmZXSvpNxp9C2BmqyV9VdJPz3jq1Hy61Mx+y90/0Y7tBgAAAIDD6E3KTjUekPR1M3uvstHZAUmv0nT/aoOkP5vLCtx9g5l9QNI7JZ0j6WYz+1NJDyjrY10h6Sl59fe7+31zfC1NHcr73G5SduHw8+aw7B9resffIel9mt5Z71C2s16v7ObHf5BqwMwqkq7XdMf2ekkflbQrL/sDSUdJutbMtrj7v85hOwEAAABgQXD3O8zslyT9jaRlkt6bqLZB0iXuvi/xXKvepawv9TplfbM7eZiDAAAVUklEQVTPJup8XHX6au3S6c7tVZLWS1rv7tvM7CRlFx23zMxOU9aBlaTvS3qmu0/d9He9mX1J0k3KviW4wsw+6e4PJJr6n5q+iPmv3P23C8/dZmY3SLpd2UG/2szOym/cDAAAAACl5O5fNrMnKRvFvUTZLXvGJd0v6XOSPuTuw/NcR03Zqc//rGzQ8VxJa5Tdjmi9pGvc/Yb5rKMV1qFredMrO7hz+yl3v7SFZT4s6bfyhxe4+62JOudLuiV/+CF3f2Oizo8knSXpMUnHpw6gmb1T0p/kD3/R3f+52fbNhpkdr/yeTs/QC9Rvg+1sHgAAACidUR/Wd/W1qYcnuPvmRvWBehZ0WnJ+re2L84f3pDq2kpSX35s/fEm+XLGd05V1bCXpHxp8M3FdYf5lc9poAAAAAMAht6A7t5JOVhYaJWWnHjcy9fzxkk6a8dyFiXqBu29Vds65JD2jtU0EAAAAABxuhzJQai7OLMzf06Ru8fkzdfC1vbNt5/GSTjCzIXc/0HQrc/lpx42sa7UtAAAAAEDrFnrn9oTCfLNz7zfVWW6u7ZiyUeB7G9RttA0AAAAAgENkoZ+WvLQwv79J3eII65IOtQMAAAAAWIAW+shtf2F+vEndscL8QIfaaWbmiPFM65RFYQMAAAAA2mihd25HC/O9Ter2FeZHZjw3s51R1deonYaaxZbPCHEGAAAAALTJQj8teV9hvtkpwkOF+ZmnHrerHQAAAADAArTQR26LI6HNkoiLpwTPDHaa2c7OFtpxNQ+fmq3K1MyYRrI1AAAAAEewsYNPlqzUqwc0s9A7t3cV5s9oUrf4/N1N2vnPFtrZNJvbALVo7dTMet3Y5qYBAACA0lsr6ceHeyNQTgv9tOSHJD2cz1/UpO4z859bJG2c8dx3C/N12zGzdcrucStJN7e2iQAAAACAw21Bj9y6u5vZFyX9pqQzzOx8d791Zj0zO1/TI65fdHef0c4GM7tb0pmSXmlmb3P34cQqLy3Mf74tL+Jgd0o6N5/fIanagXUgU0ymPlfS1sO4LZjGcVmYOC4LE8dlYeK4LEwcl4VnNsekoukzHO/s5EZhcVvQndvcByX9urJtvdrMnunu/31ivpkNSLo6fziZ10/5gKSPS1ol6X2Sfqf4pJmdKun38ocPqAOdW3cfk/T9dreLaEYy9dZmSdY4NDguCxPHZWHiuCxMHJeFieOy8MzhmHAqMuato51bM3uGpNMKRWsK86eZ2aXF+u5+3cw28lHXD0h6p6RzJN1sZn+qrAN6qqQrJD0lr/5+d7+vzuZ8StLrJD1d0m/npyB/VNJjks6T9G5JyyTVJL3R3Sdbf6UAAAAAgMOp0yO3l0v6tTrPPT2fiq6rU/ddko5S1jl9iqTPJup8XNIf1NsQd6+a2UskfU3ZqREvz6eicUm/4+431GsHAAAAALDwLPRAKUmSu9fc/TJJl0j6orKQqfH85xclvcDdL3f3WpN2dkr6GUm/pSxk6lFJo5IeVDaK+1R3/2jHXggAAAAAoCM6OnLr7pfq4JCm+bb3NWUjr/NpY1LSX+cTAAAAAGARKMXILQAAAAAAjdC5BQAAAACUHp1bAAAAAEDpmbsf7m0AAAAAAGBeGLkFAAAAAJQenVsAAAAAQOnRuQUAAAAAlB6dWwAAAABA6dG5BQAAAACUHp1bAAAAAEDp0bkFAAAAAJQenVsAAAAAQOnRuQUAAAAAlB6dW5SCmT3VzH7fzG4ws01mNmZm+81sg5ldZ2YXzrK955vZ9Wa2OW9rc/74+Z16DUcSM3ufmXlhuriFZTgmbWZma8zsHWZ2s5ltzffrw2b2PTN7v5ld0EIbF5jZZ8xso5mNmtkjZvYvZvaqQ/EaFhsz6zWzy/J9+Ejhd9m9ZvYJMzu/xXb4vDRhZkeZ2QvN7Kr8b8fOwu+k6+bQ3rz3uZl1m9kbzOzbZrbDzEbM7H4z+4iZnTXbbSqjdhwXM+s3sxeb2dX577NdZjaR/7zFzK40s2NmsU2DZvZ2M7stb2O/md1tZh8wsxPn/GJLpN2flxltD5rZg4X2Ns5iuSP6uGAO3J2JaUFPkm6S5C1Mn5bU26Qtk3RNk3aukWSH+3WXdZL0ZEkTM/bpxRyTQ34cXiFpZ5P9+oUmbfxvSdUGy39JUv/hfq1lmSSdIOkHLfwu+/N673c+L7Pa34320XWzaKct+1zSakm3NmhjVNLrDvd+W+jHRdKTJO1t4XO0V9IrW2jvVEn3NGhnt6QXHO79ttCPS5O2PzCjvY0cF6ZOTYzcogyOy38+LOkvJf2ipPMkXSDprZK25M//T0nXNWnrjyW9Pp+/Q9Kr87ZenT9W/vwftWG7jzhm1iXpo5K6JW1vcTGOSZuZ2a9K+qyyf6a3S/pDSc+V9DRJl0j6X5K+oexLiHptXJ4v1yXpAUmXKTsuL5F0Y17tRZI+1pEXsciYWbekr0o6Oy/6gaRLlf0ee56kqyQdyJ97i6TfrdMUn5e52STp63Ncdt773Mwqkq6X9NN50fWSfj5//L+UfU77JF1rZj83x+0so7kcl2WSlubzN0v6PWW/354q6eeUfdFQzev8nZn9fL2GzGyJpK9IekJe9FFJz5b0M5LeJWm/pOWSPmdmT5rldpbZfD4vBzGzp0h6s7Ivb/a1uAzHBXN3uHvXTEzNJmW/4F4pqVLn+TWS7tX0t3kX1ql3mqZHFNdLGpjx/GBe7nm9Uw/3ay/bpOwPmEu6S9J7C8fkYo7JITsGZyr7J8IlfVvS8gZ1k2c6SFoh6bG8jR9LWjPj+YqyUdup4/vMw/26F/ok6eWF/fUfqd9nyr58GM/r7JLUPeN5Pi+z2+d/KOmFko7OH5+kWY5EtWufK/siY2rdH66znj358xtmHvvFNM33uCjr4PyDpLMa1HmxpFre5v2qfybElYV1vz3x/AWF4/+tw73vFvJxqdNmRdL38zbeLWmjWhi55bgwzWc67BvAxNSOKf+FPPWL8C/r1Plwoc75deqcX6hz9eF+XWWalJ1yuS/fdxfN+ON0McfkkB2Hb+b7aodmdEpn0cbbC/v8VXXqHC9pMq/z5cP9uhf6pOxU46l9+qIG9a4v1HvijOf4vMzvGMz6n/V27XNJP9L0lxaDdeq8s9DOyw/3/lrIx6XFdv+p0O5TEs/3aPpLvLskddVp5yOFdp52uPdXmY6LsrPrXNnpxb1qoXPLcWGa78RpyVgs/r0wf+rMJ83MlH2TK0n3uPutqUby8nvzhy/Jl0Nr/krSEkmfcvebmlXmmLSfmZ2h7NQtSfqQu++cY1MvyX/uVdbZCtx9s7KOtCQ9Nz+NDPX1FuYfbFDvgcJ839QMn5dDr1373MxOlzQVFvUP7j5cZ5XXFeZfNqeNRtGNhfnwf4Gki5WdpSJlf7dqddq5rjDPcWmRmT1O2eUWkvSb7j7e4qIXi+OCeaBzi8Wi+I9j6hfhyZq+drdZx2vq+eOVfXOJJszslcpGz3cpG/VrBcek/V5RmP/c1IyZrTSz081sdbMGzKxX2fWEknRLk39Ipo5Ln6RzZ7uxR5gNhflTGtSb+ifcJd1XKOfzcui1a59fmKgXuPtWTb9PntHaJqKBvsJ86v+Clo6LstNqp66H57i07q8kDUn6jLvf2KxyAccF80LnFovFRYX5exLPn9nkedV5/sy6tSBJMrMVyoK+JOkKd9/R4qIck/abuo3MHkl3m9kvm9l/KfvSYYOknfntGN7TYKT1dGWBYBLHpZ3+XtlIuCRdkQcMHSQPXrkkf/hZd99beJrPy6HXrn0+l3ZOMLOhJnXRWFv+L3D3SU2fUcHnqQWW3SruBcpOL64XjlcPxwXzQucWpZcn9L6zUPSPiWonFOY3N2lyU53lkPY+SeuUheR8fBbLcUzab+rUx42Srpb0N8pum1F0srLroW8xs2MTbXBcOiD/0udSSSOSni5pvZn9qpmdb2bPMbP3KBul6JX0n8quVSviuBx67drnc2nHlI0CYw7M7Mma/qLoR+5+V6La1HE54O67mzQ5dVzWmllfw5pHODNbKemD+cN3unurd06YwnHBvNC5xWLwFk2fRvl5d/9+os7Swvz+Ju0dKMxzHWEDZvYMSZcrCxb6DXf3WSzOMWm/VfnPMyT9trL7AP6GpKMk9Ss7dfiGvM4Tld1GYebfAY5Lh7j75yWdo+xLoP8h6VOSblF2W6YrJQ0r69Q+Iz9FtYjjcui1a59z7A6hvJPzMWVJvZL0+3WqTh2XZsdE4rjMxvslHa3sd9tH57A8xwXzQucWpWZmF0n6v/nD7ZJ+s07V/sJ8s1CDscL8wBw3bdHLr828VtkIw1+4+52zbIJj0n5TpzH2KbvP48+7+zXuvsPdx/Ivfl6o6Q7uzygGcXBcOsTMeiS9Rtn9gVNBT0cru3fqxYnnOC6HXrv2Ocfu0PqQsi+RpCyQ6Et16k0dl1aCjjguLTCzZ0p6neb2hfcUjgvmhc4tSsvMfkrS55VdHzgm6ZXuvq1O9dHCfG+dOlOKp7aMzH0LF73fV3ady0+U3R9vtjgm7Vfcp59LpbvmyZPF0K9XN2iD49Im+fWT35T0LkmrlZ3Of6ayfbhc0vMkfVfZ6PqXzexNM5rguBx67drnHLtDxMx+T9nZRJJ0u7IzWOqZOi7NjonEcWkqHzGf+sL7L939B3NsiuOCeaFzi1Iys5MlfV3SSmUjVK9ucvuZfYX5ZqeuFEM8Wjkt5oiT33Lm9/KHb3T3A43q18Exab/iPr2hXiV3/5GkLfnDmSnHHJfO+ENJz8znL3P3K9z9Hncfd/e97v4NSc9SdvsSk/TnZla8Xprjcui1a59z7A4BM3uDpPfmD+9VduZKo79NU8elldNZOS7NvUvSE5RdB3vlPNrhuGBeuptXARaWPATnm5KOVXa7jNfl17I1UgzxaBbSUQz/2FS31pHtLcq+VX1Q0mCejDjTEwvzP2tm6/L5L+f/cHBM2m+TsnAvqbXgmuOUXY9bxHFps/y+p6/NH25w90+l6rn7pJm9W9kIble+zFvypzkuh1679vnMdhrdf3qqHVfzzzByZvZqZbeekaQfS3pOC8n9myX9tKQhM1vRJLxo6rjscPexBvWOZFfkP78p6YV1brE91RkdKvzfsN3dv1Wow3HBvNC5RamY2Rpl4StT94l8o7t/uoVFi0mJZzSpW3z+7lls3pFk6lSgU5Td4qSZdxfmT1YWAsExab8faXokNtxqZoap5ydnlG9QdjZERRyXdjla02FfdzSpe3thvrh/+bwceu3a5zPb+c8W2tk0xzNijjhm9guSPq3sC6FHJD3b3Vv5YuAuSS/P58+QFC7jyNvv1vT9p/k81Td1GvFrNf1lXj1rNP2/w02Sip1bjgvmhdOSURpmtlzSv2r6difvdPcPt7j4Q5IezucvalRR06cOblF2SxV0Bsek/b5dmD+1bq3M1BdEW4qF7j4u6bb84QV5cFg9U8dtTFIqpRyZ4hcIzb5U7qmzHJ+XQ69d+/y7hfm67eRntzw+f3hza5t4ZDOzZyu7/V+3pEclPdfdH2i81H9r6bgoC6eaGnHkuHQexwXzQucWpWBmg5K+Kump/7+9+wmVqooDOP49RiSUgVktIizIRS4KaxFPc6FhBNmiVQs3CRaBK4PCFoG2EIuC/rjojwq26R9YWbnLUKKgQlq0CCrpuWmhqVlQ9gx/Lc6ZZt5r7sz43rw/5/n9wOXdM3PnzMw53Hn3d8+5v1se2hERzw36+pKx70Ap3ppSGml4nxHaZ84PTDLT37wXERsjIvVaGJ9kam3Hc6OlDvtk+D4Czpf1iVmQ/1OyjC8pxc+7bPJh+Xt1Uz0ppRuBdaV4KCL+6LadADgN/F7WV5YRhyadB3M/t1bcX2besNo8In6gPbL0UPl/1s3GjvV+l9pc8lJKq8j9cwV5/7qv5BMY1GHgbFl/ODXMo8V+GUi/Y4JyXHC8bH684/E1E6o6jP2iKTC41ZxXRo4+AO4uD70cEU9PoqqXaI+E7EopjUsbX8q7SvEf2jch1/SxT4YoIk6R7+8IcG+3a6FTSosY346vd6lqD+2Di2dTSks6n0wpXUa+vq01tfmFqXzu+a5kqD5YijeQE6/8T0ppMdB50u6TCZu4v8y8YbV5ax+5hpwpe5yU0i20k/Qdw4P1nlJKK8j71JXky1zuj4ijvV81Xpml8kopLgee6PI+K4FNpXgkIr6Z9IfWQOwXTVXypK7mupTSftqjR58BW8jJNpqMlTPl3eraCTxVit+SDySPkadwbgXuKM/tjIimG79rACml7cC2UlwbEYcbtrNPhiildB15ivBS8sH2a8D75JGN28ht2hplejUiNjfU81h5LeT+2AF8Rw7OtpAz+wK8HREbhv9N5peSYfwo0Bq1+xh4k5yUbSEwQm7XpeX5QxGxrks97i8DSimtBpZ1PHQt8HxZ/4L2iSAAImJfQz1TbvNyQugI7ZO0+4HdwBngLnJeguuBC8ADEdGY7bx2U+2XciLgS9rJ8B4nJzHq5UREnOjyWRaRfy9b08HfAN4h31ZmLfmWd1eV8qqI6HW9dNWGtb/0eY9R4CbyyO3NPbazXzR5EeHiMqcXciB7Mctoj7oWAHv7vH4PsGC2v3ftC/lWAK02XWOfzGjbLwd+7NOme4HL+9TzDPlgu6mOg8DC2f6+tSzkadwnB/gNOwQsbqjD/WXw9t53Mf87etQzlDYnBwtf96jjb+DR2W63ud4v5OmoF3tcsL3H51lGTqTX9Nqz5BMOs952c7lfBnyPUfocp9kvLlNdnJasS0pEXIiITcB68rU6vwBj5e8B8tSmRyJPI9QMsE+GLyK+B1YATwJfka/5HCPfYuFd4J6I2BQR55trgYjYBqwG3iLf3mQMOEHOWL4hItZHxLlp+yLzTER8Sh4130q+ruwk+Rrpv8jX174HPEi+jcmZhjrcX2bYsNo8In4FVgGbyUlzTgHnyKP3u4E7I2L3tH0RdRURP5FH37eSRwt/A/4k3yv3ReD2iJh4iYCmmf2iyXJasiRJkiSpeo7cSpIkSZKqZ3ArSZIkSaqewa0kSZIkqXoGt5IkSZKk6hncSpIkSZKqZ3ArSZIkSaqewa0kSZIkqXoGt5IkSZKk6hncSpIkSZKqZ3ArSZIkSaqewa0kSZIkqXoGt5IkSZKk6hncSpIkSZKqZ3ArSZIkSaqewa0kSZIkqXoGt5IkSZKk6hncSpIkSZKqZ3ArSZIkSaqewa0kSZIkqXoGt5IkSZKk6hncSpIkSZKqZ3ArSZIkSaqewa0kSZIkqXoGt5IkSZKk6hncSpIkSZKq9y9uLux4QWqcYQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# I have found a nice release location in GIS. Let's convert it to index notation:\n",
    "seedloc = [(624464, 3347078)] # Coordinates are in meters UTM\n",
    "\n",
    "# Call the coordinate transform function\n",
    "seedind = pt.coord2ind(seedloc, \n",
    "                       (min(unstructured['x']), \n",
    "                        min(unstructured['y'])), \n",
    "                       np.shape(depth), 1.0)\n",
    "print(seedind)\n",
    "\n",
    "# Visualize the location on our array\n",
    "plt.figure(figsize=(5,5), dpi=200)\n",
    "plt.scatter(seedind[0][1], seedind[0][0], c='r')\n",
    "plt.imshow(depth)\n",
    "plt.colorbar(fraction=0.03)\n",
    "plt.title('Gridded Depth Array')\n",
    "plt.xlim([seedind[0][1]-40, seedind[0][1]+100])\n",
    "plt.ylim([seedind[0][0]+70, seedind[0][0]-30])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set up particle routing parameters\n",
    "\n",
    "Now that we have pre-converted the input data we need, let's set up the particle routing to be run. We do this using the `particle_track.modelParams` class, in which we populate the attributes to suit our application. This includes the gridded hydrodynamic outputs from above, the grid size `dx`, and tuning parameters which influence our random walk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the parameters object and then assign the values\n",
    "params = pt.modelParams()\n",
    "\n",
    "# Populate the params attributes\n",
    "params.stage = stage\n",
    "params.depth = depth\n",
    "params.qx = qx\n",
    "params.qy = qy\n",
    "\n",
    "# Other choices/parameters\n",
    "params.dx = 1. # Grid size\n",
    "params.dry_depth = 0.01 # 1 cm considered dry\n",
    "# You can also tell it which model you're using, but this only matters if the answer is DeltaRCM:\n",
    "params.model = 'Anuga'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this application, we are using the default values for the parameters of the random walk (`gamma`, `theta`, `diff_coeff`). I encourage you to play with these weights and see how your solution is affected. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate particles\n",
    "\n",
    "Now we instantiate the `particle_track.Particles` class, and generate some particles to be routed. Here we are using the `'random'` method to generate particles, which seeds them randomly within a specified region. If we knew exactly where we wanted particles, we could call the `'exact'` method instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Theta parameter not specified - using 1.0\n",
      "Gamma parameter not specified - using 0.05\n",
      "Diffusion coefficient not specified - using 0.2\n",
      "Cell Types not specified - Estimating from depth\n",
      "Using weighted random walk\n"
     ]
    }
   ],
   "source": [
    "# Now we seed in the region +/- 1 cell of the seed location we computed earlier\n",
    "# Note that \"xloc\" and \"yloc\" are x and y in the particle coordinate system!\n",
    "seed_xloc = [seedind[0][0]-1, seedind[0][0]+1]\n",
    "seed_yloc = [seedind[0][1]-1, seedind[0][1]+1]\n",
    "\n",
    "# For this example, we model 50 particles:\n",
    "Np_tracer = 50\n",
    "\n",
    "# Initialize particles and generate particles\n",
    "particles = pt.Particles(params)\n",
    "particles.generate_particles(Np_tracer, seed_xloc, seed_yloc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run the particle routing\n",
    "\n",
    "Now we call on one of the routines, `routines.steady_plots()`, to run the model. The core of the particle routing occurs in the `particle_track.run_iteration()` function, but for ease of use, we have provided several high-level wrappers for the underlying code in the `routines.py` script. These routines take common settings, run the particle routing, and save a variety of plots and data for visualization. \n",
    "\n",
    "Because our model is a steady case (i.e. flow-field is not varying with time), `steady_plots` will run the particles for an equal number of iterations and return the travel history to us in the `walk_data` dict. This dict is organized into `['xinds']`, `['yinds']`, and `['travel_times']`, which are then indexed by particle ID, and then finally iteration number. (e.g. `walk_data['xinds'][5][10]` will return the xindex for the 6th particle's 11th iteration)\n",
    "\n",
    "Note that, while this function returns a `walk_data` dictionary, this information is also stored as an attribute of the particles class, accessible via `particle.walk_data`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "  0%|                                                                                          | 0/200 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving files in existing directory\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|################################################################################| 200/200 [02:44<00:00,  1.21it/s]\n"
     ]
    }
   ],
   "source": [
    "# Using steady (time-invariant) plotting routine for 200 iterations\n",
    "walk_data = dorado.routines.steady_plots(particles, 200, 'unstructured_grid_anuga')\n",
    "# Outputs will be saved in the folder 'unstructured_grid_anuga'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because the particles take different travel paths, at any given iteration they are *not guaranteed* to be synced up in time. We can check this using the `routines.get_state()` function, which allows us to slice the `walk_data` dictionary along a given iteration number. This function logically indexes the dict like `walk_data[:][:][iteration]`, except not quite as simple given the indexing rules of a nested list. \n",
    "\n",
    "By default, this function will return the most recent step (iteration number `-1`), but we could ask it to slice along any given iteration number. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[284.1, 292.6, 301.7, 293.1, 294.2, 319.2, 275.1, 332.2, 302.7, 273.2, 303.0, 303.4, 297.2, 304.5, 281.1, 297.7, 270.8, 300.1, 299.0, 318.7, 274.5, 293.8, 288.5, 397.7, 332.9, 274.3, 271.1, 302.7, 298.6, 314.4, 317.9, 284.3, 332.5, 294.8, 313.7, 302.1, 291.5, 311.6, 326.2, 302.1, 274.0, 307.0, 275.0, 273.7, 317.2, 367.2, 272.9, 307.9, 294.5, 280.6]\n"
     ]
    }
   ],
   "source": [
    "xi, yi, ti = dorado.routines.get_state(walk_data)\n",
    "print([round(t, 1) for t in ti])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** There exists an equivalent function, `get_time_state()`, which performs a similar function by slicing `walk_data` along a given travel time, in case there is interest in viewing the particles in sync. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a brief aside, the particle routing can also be run in an *unsteady* way, in which each particle continues taking steps until each has reached a specified `target_time`. This can be useful if you want to visualize particle travel times in \"real time\", or if you want to sync up their propagation with an unsteady flow field that updates every so often (e.g. every 30 minutes). This can be done either with the `unsteady_plots()` routine, or by interacting with `run_iteration()` directly. The commented-out block of code below shows an example of what an unsteady case might look like had we used more timesteps from the model output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Specify folder to save figures:\n",
    "# path2folder = 'unstructured_grid_anuga'\n",
    "\n",
    "# # Let's say our model outputs update minute:\n",
    "# model_timestep = 60. # Units in seconds\n",
    "# # Number of steps to take in total:\n",
    "# num_steps = 20\n",
    "# # Create vector of target times\n",
    "# target_times = np.arange(model_timestep, \n",
    "#                          model_timestep*(num_steps+1), \n",
    "#                          model_timestep)\n",
    "\n",
    "# # Iterate through model timesteps\n",
    "# for i in list(range(num_steps)):\n",
    "#     # The main functional difference with an unsteady model is re-instantiating the \n",
    "#     # particle class with updated params *inside* the particle routing loop\n",
    "\n",
    "#     # Update the flow field by gridding new time-step\n",
    "#     # We don't have additional timesteps, but if we did, we update params here:\n",
    "#     params.depth = myInterp(unstructured['depth'])\n",
    "#     params.stage = myInterp(unstructured['stage'])\n",
    "#     params.qx = myInterp(unstructured['qx'])\n",
    "#     params.qy = myInterp(unstructured['qy'])\n",
    "    \n",
    "#     # Define the particle class and continue\n",
    "#     particle = pt.Particles(params)\n",
    "#     # Generate some particles\n",
    "#     if i == 0:\n",
    "#         particle.generate_particles(Np_tracer, seed_xloc, seed_yloc)\n",
    "#     else:\n",
    "#         particle.generate_particles(0, [], [], 'random', walk_data)\n",
    "    \n",
    "#     # Run the random walk for this \"model timestep\"\n",
    "#     walk_data = particle.run_iteration(target_times[i])\n",
    "\n",
    "#     # Use get_state() to return original and most recent locations \n",
    "#     x0, y0, t0 = dorado.routines.get_state(walk_data, 0) # Starting locations\n",
    "#     xi, yi, ti = dorado.routines.get_state(walk_data) # Most recent locations\n",
    "\n",
    "#     # Make and save plots and data\n",
    "#     fig = plt.figure(dpi=200)\n",
    "#     ax = fig.add_subplot(111)\n",
    "#     ax.scatter(y0, x0, c='b', s=0.75)\n",
    "#     ax.scatter(yi, xi, c='r', s=0.75)\n",
    "#     ax = plt.gca()\n",
    "#     im = ax.imshow(particle.depth)\n",
    "#     plt.title('Depth at Time ' + str(target_times[i]))\n",
    "#     cax = fig.add_axes([ax.get_position().x1+0.01,\n",
    "#                         ax.get_position().y0,\n",
    "#                         0.02,\n",
    "#                         ax.get_position().height])\n",
    "#     cbar = plt.colorbar(im, cax=cax)\n",
    "#     cbar.set_label('Water Depth [m]')\n",
    "#     plt.savefig(path2folder + '/output_by_dt'+str(i)+'.png')\n",
    "#     plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyze the outputs\n",
    "\n",
    "Now that we have the walk history stored in `walk_data`, we can query this dictionary for features of interest. For starters, we can convert the location indices back into geospatial coordinates using the function `particle_track.ind2coord()`. This will append the existing dictionary with `['xcoord']` and `['ycoord']` fields in the units we started with (meters or decimal degrees).\n",
    "\n",
    "**Note:** Particle locations are only known to within the specified grid size (i.e. $\\pm \\Delta x/2$)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(624465.25, 3347079.0)\n"
     ]
    }
   ],
   "source": [
    "# Convert particle location indices back into UTM coordinates\n",
    "walk_data = pt.ind2coord(walk_data, \n",
    "                         (min(unstructured['x']), \n",
    "                          min(unstructured['y'])), \n",
    "                         np.shape(depth), 1.0)\n",
    "\n",
    "# To check that things have worked, print starting location of first particle. \n",
    "# Should be within +/- dx from seedloc = (624464, 3347078)\n",
    "print(walk_data['xcoord'][0][0], walk_data['ycoord'][0][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For something a little more interesting, let's measure the amount of time particles spent \"exposed\" to a specific sub-region within our domain. For this we make use of the functions `particle_track.exposure_time()` and `routines.plot_exposure_time()`. If we input a binary array (same size as input arrays) delineating our region of interest (ROI) with 1's, these functions will compute and plot the exposure time distribution (ETD) of particles in this sub-region.\n",
    "\n",
    "For those familiar with the metric, the ETD is equivalent to the residence time distribution (RTD) for steady flows, with the only difference being that if particles make multiple excursions into our ROI, all those times are counted. \n",
    "\n",
    "**Note:** For a representative ETD, it is important to run a *lot* of particles. A large sample size is needed to obtain a realistic distribution (and smooth plots). Depending on the domain, I recommend at least $O(10^3)$\n",
    "\n",
    "First, let's generate and visualize the ROI:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x699778d0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the array\n",
    "regions = np.zeros_like(depth, dtype='int')\n",
    "regions[:,100:200] = 1 # Include anywhere above sea level\n",
    "\n",
    "# Visualize the region\n",
    "plt.figure(figsize=(5,5), dpi=200)\n",
    "plt.imshow(depth)\n",
    "plt.imshow(regions, cmap='bone', alpha=0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then compute. `exposure_time()` outputs a list of exposure times by particle index, and `plot_exposure_time()` will use those values to generate plots of the cumulative and differential forms of the ETD (i.e. the CDF and PDF, respectively)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|################################################################################| 50/50 [00:00<00:00, 1190.48it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving files in existing directory\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Measure exposure times\n",
    "exposure_times = pt.exposure_time(walk_data, \n",
    "                                  regions)\n",
    "# Then generate plots and save data\n",
    "exposure_times = dorado.routines.plot_exposure_time(walk_data,\n",
    "                                                    exposure_times,\n",
    "                                                    'unstructured_grid_anuga/figs', \n",
    "                                                    timedelta = 60, nbins=20)\n",
    "# Changing 'timedelta' will change the units of the time-axis. \n",
    "# Units are seconds, so 60 will plot by minute. \n",
    "# Because we are using fewer particles than ideal, smooth the plots with small 'nbins'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** If any particles are still in the ROI at the end of their travel history, they are excluded from plots. These particles are not done being \"exposed,\" so we need to run more iterations in order to capture the tail of the distribution. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
